Unsupervised.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c61ba166",
   "metadata": {},
   "source": [
    "## Using docs parsed from BBC as sample input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4c0432e",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.bbc.com/news/world-europe-61370906'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04a8aec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import urllib\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92829a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getHTML(url):\n",
    "    headers = {'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36'}\n",
    "    send_request = urllib.request.Request(url, headers=headers)\n",
    "    page = urllib.request.urlopen(send_request)\n",
    "    return page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d72d94ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "html = getHTML(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4fc0d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_source = BeautifulSoup(html.read(), 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19223297",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This video can not be played Ukrainian President Volodymyr Zelensky has accused Russia of implementing \"a bloody reconstruction of Nazism\". In a speech commemorating World War Two, he said the Russian army was replicating Nazi \"atrocities\" during its invasion of his country. \"Darkness has returned to Ukraine and it has become black and white again,\" he said in his video address.  Footage in the video showed the Ukrainian leader against a backdrop of destroyed residential buildings. \"Evil has returned, in a different uniform, under different slogans, but for the same purpose,\" he added.  The video also featured archive footage of World War Two and black-and-white footage of Russia\\'s invasion. When Ukraine was invaded by Russia in late February, Moscow maintained its operation was in part to \"de-Nazify\" the country. Russian President Vladimir Putin returned to the theme in his own address congratulating former Soviet nations on the 77th anniversary of Nazi Germany\\'s defeat, saying that \"as in 1945, victory will be ours\".  Ukrainian officials fear Russia may step up its attacks ahead of Russia\\'s Victory Day commemorations on Monday. Mr Zelensky appealed to European nations, including the UK, France and the Netherlands, by likening Nazi bombings of their towns and cities to Russian strikes on urban areas in Ukraine. Meanwhile, more than 60 people are feared dead after a Russian bomb hit a school in eastern Ukraine on Saturday, local authorities said.  The governor of Ukraine\\'s Luhansk region, Serhiy Haidai, said around 90 local residents had been sheltering in the school in the village of Bilohorivka, near the frontline in the Donbas region.  \\'If this is true, then I am also a Nazi\\' \\'We tried not to watch\\': Terror in Russian-occupied city Ukraine war: Returning to the place my father was killed Sweden confirms it will apply to join Nato The children\\'s camp that became an execution ground Buffalo gunman planned second attack, police say What Indigenous Australians want from the election. VideoWhat Indigenous Australians want from the election Pinned down by Russian fire in key frontline village Why Urdu language draws ire of India’s right-wing The children\\'s camp that became an execution ground Can Africa replace Russian gas supplies to Europe? How Australia became the anxious country The new jet set - why private plane use has soared A tale of two women’s murders – and media bias What’s the difference between love and lust? Russia pushed back from Kharkiv - front line report Correspondent Quentin Sommerville and cameraman Darren Conway are with Ukrainian troops as they advance. Russia fighting for motherland in Ukraine - Putin The Russian leader uses Victory Day to tie the war in Ukraine to the fight against Nazi Germany LISTEN: The extraordinary story of Putin\\'s life A revealing history as told by those who have watched, studied and worked with the Russian president © 2022 BBC. The BBC is not responsible for the content of external sites. Read about our approach to external linking.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text1 = ' '.join([i.text for i in page_source.find_all('p')])\n",
    "text1.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b3ee622b",
   "metadata": {},
   "outputs": [],
   "source": [
    "text2 = 'US President Joe Biden has accused Russian forces of committing acts of genocide in Ukraine. \\\n",
    "He said Russian President Vladimir Putin was trying to \"wipe out the idea\" of a Ukrainian identity. \\\n",
    "The US has historically been reluctant to use the word genocide due to a duty to intervene if the term is used.\\\n",
    "French President Emmanuel Macron later said he was reluctant to use the word. The Kremlin called Mr Biden\\'s comments \"unacceptable\". But Mr Biden insisted on\\\n",
    "Tuesday night that evidence of genocidal acts by Russian troops was mounting. He first made the comments as part of a throwaway remark during a speech in Iowa about increasing inflation,\\\n",
    "telling supporters in Iowa their ability to budget should not \"hinge on whether a dictator declares war and commits genocide half a world away\".\\\n",
    "Speaking to the public broadcaster France 2, the President Macron said he would be \"careful with such terms today because these two peoples are brothers.\"\\\n",
    "\"I want to continue to try, as much as I can, to stop this war and rebuild peace. I am not sure that an escalation of rhetoric serves that cause,\" he added.\\\n",
    "The Kremlin said Mr Biden was attempting to \"distort the situation\" in Ukraine.\\\n",
    "\"This is hardly acceptable from a president of the United States, a country that has committed well-known \\\n",
    "crimes in recent times,\" Kremlin spokesperson Dmitry Peskov said on a conference call with reporters.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6cf60e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install svgling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "822bc421",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11b4e2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "amazon_reviewDF = pd.read_csv('./Amazon_review.csv', delimiter=',', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3603ed55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text     label\n",
       "0  Stuning even for the non-gamer: This sound tra...  Positive\n",
       "1  The best soundtrack ever to anything.: I'm rea...  Positive\n",
       "2  Amazing!: This soundtrack is my favorite music...  Positive\n",
       "3  Excellent Soundtrack: I truly like this soundt...  Positive\n",
       "4  Remember, Pull Your Jaw Off The Floor After He...  Positive"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amazon_reviewDF.columns = ['text', 'label']; amazon_reviewDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f40cf40a",
   "metadata": {},
   "source": [
    "## NER 命名實體識別\n",
    "- **NLTK ner**\n",
    "- **Stanford ner**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7200eb",
   "metadata": {},
   "source": [
    "### NLTK ner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7d1f1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "80057d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_document(doc):\n",
    "    doc = re.sub('\\n', ' ', doc)\n",
    "    doc = re.sub('\"', ' ', doc)\n",
    "    if isinstance(doc, str):\n",
    "        doc = doc\n",
    "    else:\n",
    "        raise ValueError('Document is not string.')\n",
    "    \n",
    "    # remove the white space from top till the end\n",
    "    doc = doc.strip()\n",
    "    # splitting sentences\n",
    "    sentences = nltk.sent_tokenize(doc)\n",
    "    sentences = [sentence.strip() for sentence in sentences]\n",
    "    \n",
    "    return sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97e5329e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This',\n",
       " 'video',\n",
       " 'can',\n",
       " 'not',\n",
       " 'be',\n",
       " 'played',\n",
       " 'Ukrainian',\n",
       " 'President',\n",
       " 'Volodymyr',\n",
       " 'Zelensky',\n",
       " 'has',\n",
       " 'accused',\n",
       " 'Russia',\n",
       " 'of',\n",
       " 'implementing',\n",
       " 'a',\n",
       " 'bloody',\n",
       " 'reconstruction',\n",
       " 'of',\n",
       " 'Nazism',\n",
       " '.']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tokenize sentences\n",
    "sentences = parse_document(text1)\n",
    "tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]\n",
    "tokenized_sentences[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c4acb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tagged sentences and use nltk named entity chunker\n",
    "# nltk.pos_tag: 詞性標籤\n",
    "tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]\n",
    "ne_chunk_sents = [nltk.ne_chunk(tagged) for tagged in tagged_sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b42172b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  This/DT\n",
      "  video/NN\n",
      "  can/MD\n",
      "  not/RB\n",
      "  be/VB\n",
      "  played/VBN\n",
      "  (GPE Ukrainian/JJ)\n",
      "  President/NNP\n",
      "  (PERSON Volodymyr/NNP Zelensky/NNP)\n",
      "  has/VBZ\n",
      "  accused/VBN\n",
      "  (GPE Russia/NNP)\n",
      "  of/IN\n",
      "  implementing/VBG\n",
      "  a/DT\n",
      "  bloody/JJ\n",
      "  reconstruction/NN\n",
      "  of/IN\n",
      "  (GPE Nazism/NNP)\n",
      "  ./.)\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"168px\" preserveAspectRatio=\"xMidYMid meet\" style=\"font-family: times, serif; font-weight:normal; font-style: normal; font-size: 16px;\" version=\"1.1\" viewBox=\"0,0,1288.0,168.0\" width=\"1288px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">S</text></svg><svg width=\"3.72671%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">This</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">DT</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"1.86335%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.34783%\" x=\"3.72671%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">video</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"5.90062%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"3.10559%\" x=\"8.07453%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">can</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">MD</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"9.62733%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"3.10559%\" x=\"11.1801%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">not</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">RB</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"12.7329%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"2.48447%\" x=\"14.2857%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">be</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VB</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"15.528%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.96894%\" x=\"16.7702%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">played</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VBN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"19.2547%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"6.8323%\" x=\"21.7391%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">GPE</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">Ukrainian</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">JJ</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"25.1553%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"6.8323%\" x=\"28.5714%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">President</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NNP</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"31.9876%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"13.0435%\" x=\"35.4037%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">PERSON</text></svg><svg width=\"52.381%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">Volodymyr</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NNP</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"26.1905%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"47.619%\" x=\"52.381%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">Zelensky</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NNP</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"76.1905%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"41.9255%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"3.10559%\" x=\"48.4472%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">has</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VBZ</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"5.59006%\" x=\"51.5528%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">accused</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VBN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"54.3478%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.96894%\" x=\"57.1429%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">GPE</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">Russia</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NNP</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"59.6273%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"2.48447%\" x=\"62.1118%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">of</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">IN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"63.354%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"8.69565%\" x=\"64.5963%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">implementing</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VBG</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"68.9441%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"2.48447%\" x=\"73.2919%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">a</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">DT</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"74.5342%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.96894%\" x=\"75.7764%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">bloody</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">JJ</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"78.2609%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"9.93789%\" x=\"80.7453%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">reconstruction</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"85.7143%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"2.48447%\" x=\"90.6832%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">of</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">IN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"91.9255%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.96894%\" x=\"93.1677%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">GPE</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">Nazism</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NNP</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"95.6522%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"1.86335%\" x=\"98.1366%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">.</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">.</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"99.0683%\" y1=\"1.2em\" y2=\"3em\" /></svg>"
      ],
      "text/plain": [
       "Tree('S', [('This', 'DT'), ('video', 'NN'), ('can', 'MD'), ('not', 'RB'), ('be', 'VB'), ('played', 'VBN'), Tree('GPE', [('Ukrainian', 'JJ')]), ('President', 'NNP'), Tree('PERSON', [('Volodymyr', 'NNP'), ('Zelensky', 'NNP')]), ('has', 'VBZ'), ('accused', 'VBN'), Tree('GPE', [('Russia', 'NNP')]), ('of', 'IN'), ('implementing', 'VBG'), ('a', 'DT'), ('bloody', 'JJ'), ('reconstruction', 'NN'), ('of', 'IN'), Tree('GPE', [('Nazism', 'NNP')]), ('.', '.')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(nltk.ne_chunk(tagged_sentences[0]))\n",
    "nltk.ne_chunk(tagged_sentences[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6974cf4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This video can not be played Ukrainian President Volodymyr Zelensky has accused Russia of implementing a bloody reconstruction of Nazism .\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(' '.join([i[0] for i in ne_chunk_sents[0].leaves()]))\n",
    "ne_chunk_sents[5].label()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f4047510",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity Name</th>\n",
       "      <th>Entity Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ukrainian</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vladimir Putin</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indigenous Australians</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Darren Conway</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>European</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Victory</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>India</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Russia</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BBC</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bilohorivka</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Footage</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Australia</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Donbas</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>UK</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Europe</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Serhiy Haidai</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Darkness</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Urdu</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nazism</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Russian</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nazi Germany</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Quentin Sommerville</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Volodymyr Zelensky</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Soviet</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Evil</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Kharkiv</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Putin</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Zelensky</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Nazi</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Luhansk</td>\n",
       "      <td>GPE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Entity Name   Entity Type\n",
       "0                Ukrainian           GPE\n",
       "1           Vladimir Putin        PERSON\n",
       "2   Indigenous Australians  ORGANIZATION\n",
       "3            Darren Conway        PERSON\n",
       "4                   France           GPE\n",
       "5                 European           GPE\n",
       "6                  Victory        PERSON\n",
       "7                    India           GPE\n",
       "8                   Russia           GPE\n",
       "9                  Ukraine        PERSON\n",
       "10                     BBC  ORGANIZATION\n",
       "11             Bilohorivka           GPE\n",
       "12                 Ukraine           GPE\n",
       "13                 Footage           GPE\n",
       "14               Australia        PERSON\n",
       "15                 Buffalo        PERSON\n",
       "16                  Donbas           GPE\n",
       "17                      UK  ORGANIZATION\n",
       "18                  Europe           GPE\n",
       "19             Netherlands           GPE\n",
       "20           Serhiy Haidai        PERSON\n",
       "21                Darkness           GPE\n",
       "22                    Urdu        PERSON\n",
       "23                  Nazism           GPE\n",
       "24                 Russian           GPE\n",
       "25                  Moscow           GPE\n",
       "26            Nazi Germany  ORGANIZATION\n",
       "27     Quentin Sommerville        PERSON\n",
       "28      Volodymyr Zelensky        PERSON\n",
       "29                  Soviet           GPE\n",
       "30                    Evil           GPE\n",
       "31                 Kharkiv           GPE\n",
       "32                   Putin        PERSON\n",
       "33                Zelensky        PERSON\n",
       "34                    Nazi  ORGANIZATION\n",
       "35                  Sweden        PERSON\n",
       "36                 Luhansk           GPE"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all named entites\n",
    "named_entities = []\n",
    "for ne_tagged_sentences in ne_chunk_sents:\n",
    "    # ne_tagged_sentences is a tree\n",
    "    \n",
    "    for tagged_tree in ne_tagged_sentences:\n",
    "        # tagged_tree is either a tree or a tuple with words and its ner\n",
    "        # I only extract chunks having NE labels\n",
    "        \n",
    "        if hasattr(tagged_tree, 'label'): # if tagged tree has an attr name called 'label'\n",
    "            entity_name = ' '.join(c[0] for c in tagged_tree.leaves())\n",
    "            entity_type = tagged_tree.label() # get NE category\n",
    "            named_entities.append((entity_name, entity_type))\n",
    "\n",
    "            # get unique named entities\n",
    "            named_entities = list(set(named_entities))\n",
    "            \n",
    "# store named entities in a data frame\n",
    "entity_frame = pd.DataFrame(named_entities, columns=['Entity Name', 'Entity Type'])\n",
    "entity_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f93ba6",
   "metadata": {},
   "source": [
    "**可以看出，NLTK中的任務大致上有成功識別出一些國家地域及人名，但仍有很多不太如人意之處。**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643b0b7c",
   "metadata": {},
   "source": [
    "### Stanford ner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c1fd8876",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from nltk.tag import StanfordNERTagger\n",
    "import os\n",
    "import pandas as pd\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f502e94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity Name</th>\n",
       "      <th>Entity Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilohorivka</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vladimir Putin</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Darren Conway</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nazi Germany</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Putin</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Europe</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1945</td>\n",
       "      <td>DATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BBC</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Urdu</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kharkiv</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>UK</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Luhansk</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Serhiy Haidai</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Quentin Sommerville</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Volodymyr Zelensky</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>DATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>France</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>India</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Russia</td>\n",
       "      <td>LOCATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Zelensky</td>\n",
       "      <td>PERSON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nazi</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>February</td>\n",
       "      <td>DATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2022 BBC</td>\n",
       "      <td>ORGANIZATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Monday</td>\n",
       "      <td>DATE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Entity Name   Entity Type\n",
       "0           Bilohorivka      LOCATION\n",
       "1        Vladimir Putin        PERSON\n",
       "2                Africa      LOCATION\n",
       "3               Ukraine      LOCATION\n",
       "4         Darren Conway        PERSON\n",
       "5          Nazi Germany      LOCATION\n",
       "6                 Putin        PERSON\n",
       "7                Europe      LOCATION\n",
       "8                  1945          DATE\n",
       "9             Australia      LOCATION\n",
       "10              Buffalo      LOCATION\n",
       "11               Moscow      LOCATION\n",
       "12                  BBC  ORGANIZATION\n",
       "13      the Netherlands      LOCATION\n",
       "14                 Urdu      LOCATION\n",
       "15              Kharkiv      LOCATION\n",
       "16                   UK  ORGANIZATION\n",
       "17              Luhansk      LOCATION\n",
       "18        Serhiy Haidai        PERSON\n",
       "19               Sweden      LOCATION\n",
       "20  Quentin Sommerville        PERSON\n",
       "21   Volodymyr Zelensky        PERSON\n",
       "22             Saturday          DATE\n",
       "23               France      LOCATION\n",
       "24                India      LOCATION\n",
       "25               Russia      LOCATION\n",
       "26             Zelensky        PERSON\n",
       "27                 Nazi  ORGANIZATION\n",
       "28             February          DATE\n",
       "29             2022 BBC  ORGANIZATION\n",
       "30               Monday          DATE"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def parse_doc(doc):\n",
    "    doc = re.sub('\\n', ' ', doc)\n",
    "    if isinstance(doc, str):\n",
    "        pass\n",
    "    else:\n",
    "        raise ValueError('Doc is not a string.')\n",
    "        \n",
    "    doc = doc.strip()\n",
    "    sentences = nltk.sent_tokenize(doc)\n",
    "    sentences = [sentence.strip() for sentence in sentences]\n",
    "    \n",
    "    return sentences\n",
    "\n",
    "\n",
    "sentences = parse_doc(text1)\n",
    "tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]\n",
    "\n",
    "java_path = 'C:\\\\Program Files\\\\Java\\\\jdk-18.0.1.1\\\\bin\\\\java.exe'\n",
    "os.environ['JAVAHOME'] = java_path\n",
    "# load stanford NER\n",
    "sn = StanfordNERTagger('D:\\\\stanford-ner-2017-06-09\\\\stanford-ner-2017-06-09\\\\classifiers\\\\english.muc.7class.distsim.crf.ser.gz',\n",
    "                       path_to_jar='D:\\\\stanford-ner-2017-06-09\\\\stanford-ner-2017-06-09\\\\stanford-ner.jar')\n",
    "# tag sentence\n",
    "ne_annotated_sentence = [sn.tag(sentence) for sentence in tokenized_sentences]\n",
    "\n",
    "named_entity_frame=[]\n",
    "# extract named entities\n",
    "for term_tag in ne_annotated_sentence:\n",
    "    \n",
    "    temp_entity_name=''\n",
    "    temp_named_entity = None\n",
    "    \n",
    "    for term, tag in term_tag:\n",
    "        # get terms with NE tags\n",
    "        if tag != 'O':\n",
    "            temp_entity_name = ' '.join([temp_entity_name, term]).strip() # combining NE tagged terms into a sentence\n",
    "            temp_named_entity = (temp_entity_name, tag) # get NE and its category\n",
    "        else:\n",
    "            if temp_named_entity:\n",
    "                named_entity_frame.append(temp_named_entity)\n",
    "                temp_entity_name=''\n",
    "                temp_named_entity = None\n",
    "\n",
    "# get unique named entities\n",
    "named_entity_frame = list(set(named_entity_frame))\n",
    "named_entity_frame = pd.DataFrame(named_entity_frame, columns=['Entity Name', 'Entity Type'])\n",
    "\n",
    "named_entity_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59b21ff1",
   "metadata": {},
   "source": [
    "**實作兩個NER可以看出在Stanford NER的幫助下，命名實體識別的效果較佳，但這並不是說Stanford NER一定會比NLTK NER的效果好! 因為兩者的演算法有所差異，因此需要根據自己的需求去決定使用什麼工具。**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d6cdc2",
   "metadata": {},
   "source": [
    "## Spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b306d31",
   "metadata": {},
   "source": [
    "- **Spacy的統計模型**\n",
    "- **Spacy的處理管道**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98376b19",
   "metadata": {},
   "source": [
    "模型是spacy的核心，spacy會利用這些模型執行一些與NLP相關的任務。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e0698914",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "# 不去 load spacy model中兩個套件: parser(解析器), ner(命名實體識別), ...\n",
    "nlp = spacy.load('en_core_web_sm', disable=['parser','attribute_ruler', 'lemmatizer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6416d59d",
   "metadata": {},
   "source": [
    "建立NLP物件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "00214363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "He wants to play basketball."
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = nlp('He wants to play basketball.'); doc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cfc4c26",
   "metadata": {},
   "source": [
    "找出活動的管道元件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e78e9b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tok2vec', 'tagger', 'ner']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.pipe_names # functions of spacy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5109304b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tok2vec', 'ner']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 禁用管道元件\n",
    "nlp.disable_pipes('tagger'); nlp.pipe_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1417b892",
   "metadata": {},
   "source": [
    "### 1. 詞性標註"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8ef506c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This --> DET\n",
      "video --> NOUN\n",
      "can --> AUX\n",
      "not --> PART\n",
      "be --> AUX\n",
      "played --> VERB\n",
      "Ukrainian --> ADJ\n",
      "President --> PROPN\n",
      "Volodymyr --> PROPN\n",
      "Zelensky --> PROPN\n",
      "has --> AUX\n",
      "accused --> VERB\n",
      "Russia --> PROPN\n",
      "of --> ADP\n",
      "implementing --> VERB\n",
      "\" --> PUNCT\n",
      "a --> DET\n",
      "bloody --> ADJ\n",
      "reconstruction --> NOUN\n",
      "of --> ADP\n",
      "Nazism --> PROPN\n",
      "\" --> PUNCT\n",
      ". --> PUNCT\n",
      "In --> ADP\n",
      "a --> DET\n",
      "speech --> NOUN\n",
      "commemorating --> VERB\n",
      "World --> PROPN\n",
      "War --> PROPN\n",
      "Two --> PROPN\n",
      ", --> PUNCT\n",
      "he --> PRON\n",
      "said --> VERB\n",
      "the --> DET\n",
      "Russian --> ADJ\n",
      "army --> NOUN\n",
      "was --> AUX\n",
      "replicating --> VERB\n",
      "Nazi --> PROPN\n",
      "\" --> PUNCT\n",
      "atrocities --> NOUN\n",
      "\" --> PUNCT\n",
      "during --> ADP\n",
      "its --> PRON\n",
      "invasion --> NOUN\n",
      "of --> ADP\n",
      "his --> PRON\n",
      "country --> NOUN\n",
      ". --> PUNCT\n",
      "\" --> PUNCT\n",
      "Darkness --> NOUN\n",
      "has --> AUX\n",
      "returned --> VERB\n",
      "to --> ADP\n",
      "Ukraine --> PROPN\n",
      "and --> CCONJ\n",
      "it --> PRON\n",
      "has --> AUX\n",
      "become --> VERB\n",
      "black --> ADJ\n",
      "and --> CCONJ\n",
      "white --> ADJ\n",
      "again --> ADV\n",
      ", --> PUNCT\n",
      "\" --> PUNCT\n",
      "he --> PRON\n",
      "said --> VERB\n",
      "in --> ADP\n",
      "his --> PRON\n",
      "video --> NOUN\n",
      "address --> NOUN\n",
      ". --> PUNCT\n",
      "  --> SPACE\n",
      "Footage --> PROPN\n",
      "in --> ADP\n",
      "the --> DET\n",
      "video --> NOUN\n",
      "showed --> VERB\n",
      "the --> DET\n",
      "Ukrainian --> ADJ\n",
      "leader --> NOUN\n",
      "against --> ADP\n",
      "a --> DET\n",
      "backdrop --> NOUN\n",
      "of --> ADP\n",
      "destroyed --> VERB\n",
      "residential --> ADJ\n",
      "buildings --> NOUN\n",
      ". --> PUNCT\n",
      "\" --> PUNCT\n",
      "Evil --> NOUN\n",
      "has --> AUX\n",
      "returned --> VERB\n",
      ", --> PUNCT\n",
      "in --> ADP\n",
      "a --> DET\n",
      "different --> ADJ\n",
      "uniform --> NOUN\n",
      ", --> PUNCT\n",
      "under --> ADP\n",
      "different --> ADJ\n",
      "slogans --> NOUN\n",
      ", --> PUNCT\n",
      "but --> CCONJ\n",
      "for --> ADP\n",
      "the --> DET\n",
      "same --> ADJ\n",
      "purpose --> NOUN\n",
      ", --> PUNCT\n",
      "\" --> PUNCT\n",
      "he --> PRON\n",
      "added --> VERB\n",
      ". --> PUNCT\n",
      "  --> SPACE\n",
      "The --> DET\n",
      "video --> NOUN\n",
      "also --> ADV\n",
      "featured --> VERB\n",
      "archive --> NOUN\n",
      "footage --> NOUN\n",
      "of --> ADP\n",
      "World --> PROPN\n",
      "War --> PROPN\n",
      "Two --> NUM\n",
      "and --> CCONJ\n",
      "black --> ADJ\n",
      "- --> PUNCT\n",
      "and --> CCONJ\n",
      "- --> PUNCT\n",
      "white --> ADJ\n",
      "footage --> NOUN\n",
      "of --> ADP\n",
      "Russia --> PROPN\n",
      "'s --> PART\n",
      "invasion --> NOUN\n",
      ". --> PUNCT\n",
      "When --> SCONJ\n",
      "Ukraine --> PROPN\n",
      "was --> AUX\n",
      "invaded --> VERB\n",
      "by --> ADP\n",
      "Russia --> PROPN\n",
      "in --> ADP\n",
      "late --> ADJ\n",
      "February --> PROPN\n",
      ", --> PUNCT\n",
      "Moscow --> PROPN\n",
      "maintained --> VERB\n",
      "its --> PRON\n",
      "operation --> NOUN\n",
      "was --> AUX\n",
      "in --> ADP\n",
      "part --> NOUN\n",
      "to --> ADP\n",
      "\" --> PUNCT\n",
      "de --> ADJ\n",
      "- --> ADJ\n",
      "Nazify --> VERB\n",
      "\" --> PUNCT\n",
      "the --> DET\n",
      "country --> NOUN\n",
      ". --> PUNCT\n",
      "Russian --> ADJ\n",
      "President --> PROPN\n",
      "Vladimir --> PROPN\n",
      "Putin --> PROPN\n",
      "returned --> VERB\n",
      "to --> ADP\n",
      "the --> DET\n",
      "theme --> NOUN\n",
      "in --> ADP\n",
      "his --> PRON\n",
      "own --> ADJ\n",
      "address --> NOUN\n",
      "congratulating --> VERB\n",
      "former --> ADJ\n",
      "Soviet --> ADJ\n",
      "nations --> NOUN\n",
      "on --> ADP\n",
      "the --> DET\n",
      "77th --> ADJ\n",
      "anniversary --> NOUN\n",
      "of --> ADP\n",
      "Nazi --> PROPN\n",
      "Germany --> PROPN\n",
      "'s --> PART\n",
      "defeat --> NOUN\n",
      ", --> PUNCT\n",
      "saying --> VERB\n",
      "that --> SCONJ\n",
      "\" --> PUNCT\n",
      "as --> ADP\n",
      "in --> ADP\n",
      "1945 --> NUM\n",
      ", --> PUNCT\n",
      "victory --> NOUN\n",
      "will --> AUX\n",
      "be --> AUX\n",
      "ours --> PRON\n",
      "\" --> PUNCT\n",
      ". --> PUNCT\n",
      "  --> SPACE\n",
      "Ukrainian --> ADJ\n",
      "officials --> NOUN\n",
      "fear --> VERB\n",
      "Russia --> PROPN\n",
      "may --> AUX\n",
      "step --> VERB\n",
      "up --> ADP\n",
      "its --> PRON\n",
      "attacks --> NOUN\n",
      "ahead --> ADV\n",
      "of --> ADP\n",
      "Russia --> PROPN\n",
      "'s --> PART\n",
      "Victory --> PROPN\n",
      "Day --> PROPN\n",
      "commemorations --> NOUN\n",
      "on --> ADP\n",
      "Monday --> PROPN\n",
      ". --> PUNCT\n",
      "Mr --> PROPN\n",
      "Zelensky --> PROPN\n",
      "appealed --> VERB\n",
      "to --> ADP\n",
      "European --> ADJ\n",
      "nations --> NOUN\n",
      ", --> PUNCT\n",
      "including --> VERB\n",
      "the --> DET\n",
      "UK --> PROPN\n",
      ", --> PUNCT\n",
      "France --> PROPN\n",
      "and --> CCONJ\n",
      "the --> DET\n",
      "Netherlands --> PROPN\n",
      ", --> PUNCT\n",
      "by --> ADP\n",
      "likening --> VERB\n",
      "Nazi --> ADJ\n",
      "bombings --> NOUN\n",
      "of --> ADP\n",
      "their --> PRON\n",
      "towns --> NOUN\n",
      "and --> CCONJ\n",
      "cities --> NOUN\n",
      "to --> ADP\n",
      "Russian --> ADJ\n",
      "strikes --> NOUN\n",
      "on --> ADP\n",
      "urban --> ADJ\n",
      "areas --> NOUN\n",
      "in --> ADP\n",
      "Ukraine --> PROPN\n",
      ". --> PUNCT\n",
      "Meanwhile --> ADV\n",
      ", --> PUNCT\n",
      "more --> ADJ\n",
      "than --> ADP\n",
      "60 --> NUM\n",
      "people --> NOUN\n",
      "are --> AUX\n",
      "feared --> VERB\n",
      "dead --> ADJ\n",
      "after --> SCONJ\n",
      "a --> DET\n",
      "Russian --> ADJ\n",
      "bomb --> NOUN\n",
      "hit --> VERB\n",
      "a --> DET\n",
      "school --> NOUN\n",
      "in --> ADP\n",
      "eastern --> ADJ\n",
      "Ukraine --> PROPN\n",
      "on --> ADP\n",
      "Saturday --> PROPN\n",
      ", --> PUNCT\n",
      "local --> ADJ\n",
      "authorities --> NOUN\n",
      "said --> VERB\n",
      ". --> PUNCT\n",
      "  --> SPACE\n",
      "The --> DET\n",
      "governor --> NOUN\n",
      "of --> ADP\n",
      "Ukraine --> PROPN\n",
      "'s --> PART\n",
      "Luhansk --> PROPN\n",
      "region --> NOUN\n",
      ", --> PUNCT\n",
      "Serhiy --> PROPN\n",
      "Haidai --> PROPN\n",
      ", --> PUNCT\n",
      "said --> VERB\n",
      "around --> ADV\n",
      "90 --> NUM\n",
      "local --> ADJ\n",
      "residents --> NOUN\n",
      "had --> AUX\n",
      "been --> AUX\n",
      "sheltering --> VERB\n",
      "in --> ADP\n",
      "the --> DET\n",
      "school --> NOUN\n",
      "in --> ADP\n",
      "the --> DET\n",
      "village --> NOUN\n",
      "of --> ADP\n",
      "Bilohorivka --> PROPN\n",
      ", --> PUNCT\n",
      "near --> ADP\n",
      "the --> DET\n",
      "frontline --> NOUN\n",
      "in --> ADP\n",
      "the --> DET\n",
      "Donbas --> PROPN\n",
      "region --> NOUN\n",
      ". --> PUNCT\n",
      "  --> SPACE\n",
      "' --> PUNCT\n",
      "If --> SCONJ\n",
      "this --> PRON\n",
      "is --> AUX\n",
      "true --> ADJ\n",
      ", --> PUNCT\n",
      "then --> ADV\n",
      "I --> PRON\n",
      "am --> AUX\n",
      "also --> ADV\n",
      "a --> DET\n",
      "Nazi --> PROPN\n",
      "' --> PUNCT\n",
      "' --> PUNCT\n",
      "We --> PRON\n",
      "tried --> VERB\n",
      "not --> PART\n",
      "to --> PART\n",
      "watch --> VERB\n",
      "' --> PUNCT\n",
      ": --> PUNCT\n",
      "Terror --> PROPN\n",
      "in --> ADP\n",
      "Russian --> PROPN\n",
      "- --> PUNCT\n",
      "occupied --> VERB\n",
      "city --> NOUN\n",
      "Ukraine --> PROPN\n",
      "war --> NOUN\n",
      ": --> PUNCT\n",
      "Returning --> VERB\n",
      "to --> ADP\n",
      "the --> DET\n",
      "place --> NOUN\n",
      "my --> PRON\n",
      "father --> NOUN\n",
      "was --> AUX\n",
      "killed --> VERB\n",
      "Sweden --> PROPN\n",
      "confirms --> VERB\n",
      "it --> PRON\n",
      "will --> AUX\n",
      "apply --> VERB\n",
      "to --> PART\n",
      "join --> VERB\n",
      "Nato --> PROPN\n",
      "The --> DET\n",
      "children --> NOUN\n",
      "'s --> PART\n",
      "camp --> NOUN\n",
      "that --> PRON\n",
      "became --> VERB\n",
      "an --> DET\n",
      "execution --> NOUN\n",
      "ground --> NOUN\n",
      "Buffalo --> PROPN\n",
      "gunman --> NOUN\n",
      "planned --> VERB\n",
      "second --> ADJ\n",
      "attack --> NOUN\n",
      ", --> PUNCT\n",
      "police --> NOUN\n",
      "say --> VERB\n",
      "What --> PRON\n",
      "Indigenous --> PROPN\n",
      "Australians --> PROPN\n",
      "want --> VERB\n",
      "from --> ADP\n",
      "the --> DET\n",
      "election --> NOUN\n",
      ". --> PUNCT\n",
      "VideoWhat --> NOUN\n",
      "Indigenous --> PROPN\n",
      "Australians --> PROPN\n",
      "want --> VERB\n",
      "from --> ADP\n",
      "the --> DET\n",
      "election --> NOUN\n",
      "Pinned --> VERB\n",
      "down --> ADP\n",
      "by --> ADP\n",
      "Russian --> ADJ\n",
      "fire --> NOUN\n",
      "in --> ADP\n",
      "key --> ADJ\n",
      "frontline --> NOUN\n",
      "village --> NOUN\n",
      "Why --> SCONJ\n",
      "Urdu --> PROPN\n",
      "language --> NOUN\n",
      "draws --> VERB\n",
      "ire --> NOUN\n",
      "of --> ADP\n",
      "India --> PROPN\n",
      "’s --> PART\n",
      "right --> ADJ\n",
      "- --> PUNCT\n",
      "wing --> NOUN\n",
      "The --> DET\n",
      "children --> NOUN\n",
      "'s --> PART\n",
      "camp --> NOUN\n",
      "that --> PRON\n",
      "became --> VERB\n",
      "an --> DET\n",
      "execution --> NOUN\n",
      "ground --> NOUN\n",
      "Can --> AUX\n",
      "Africa --> PROPN\n",
      "replace --> VERB\n",
      "Russian --> ADJ\n",
      "gas --> NOUN\n",
      "supplies --> NOUN\n",
      "to --> ADP\n",
      "Europe --> PROPN\n",
      "? --> PUNCT\n",
      "How --> SCONJ\n",
      "Australia --> PROPN\n",
      "became --> VERB\n",
      "the --> DET\n",
      "anxious --> ADJ\n",
      "country --> NOUN\n",
      "The --> DET\n",
      "new --> ADJ\n",
      "jet --> NOUN\n",
      "set --> NOUN\n",
      "- --> PUNCT\n",
      "why --> SCONJ\n",
      "private --> ADJ\n",
      "plane --> NOUN\n",
      "use --> NOUN\n",
      "has --> AUX\n",
      "soared --> VERB\n",
      "A --> DET\n",
      "tale --> NOUN\n",
      "of --> ADP\n",
      "two --> NUM\n",
      "women --> NOUN\n",
      "’s --> PART\n",
      "murders --> NOUN\n",
      "– --> PUNCT\n",
      "and --> CCONJ\n",
      "media --> NOUN\n",
      "bias --> NOUN\n",
      "What --> PRON\n",
      "’s --> VERB\n",
      "the --> DET\n",
      "difference --> NOUN\n",
      "between --> ADP\n",
      "love --> NOUN\n",
      "and --> CCONJ\n",
      "lust --> NOUN\n",
      "? --> PUNCT\n",
      "Russia --> PROPN\n",
      "pushed --> VERB\n",
      "back --> ADV\n",
      "from --> ADP\n",
      "Kharkiv --> PROPN\n",
      "- --> PUNCT\n",
      "front --> ADJ\n",
      "line --> NOUN\n",
      "report --> NOUN\n",
      "Correspondent --> PROPN\n",
      "Quentin --> PROPN\n",
      "Sommerville --> PROPN\n",
      "and --> CCONJ\n",
      "cameraman --> PROPN\n",
      "Darren --> PROPN\n",
      "Conway --> PROPN\n",
      "are --> AUX\n",
      "with --> ADP\n",
      "Ukrainian --> ADJ\n",
      "troops --> NOUN\n",
      "as --> SCONJ\n",
      "they --> PRON\n",
      "advance --> VERB\n",
      ". --> PUNCT\n",
      "Russia --> PROPN\n",
      "fighting --> VERB\n",
      "for --> ADP\n",
      "motherland --> NOUN\n",
      "in --> ADP\n",
      "Ukraine --> PROPN\n",
      "- --> PUNCT\n",
      "Putin --> PROPN\n",
      "The --> DET\n",
      "Russian --> ADJ\n",
      "leader --> NOUN\n",
      "uses --> VERB\n",
      "Victory --> PROPN\n",
      "Day --> PROPN\n",
      "to --> PART\n",
      "tie --> VERB\n",
      "the --> DET\n",
      "war --> NOUN\n",
      "in --> ADP\n",
      "Ukraine --> PROPN\n",
      "to --> ADP\n",
      "the --> DET\n",
      "fight --> NOUN\n",
      "against --> ADP\n",
      "Nazi --> PROPN\n",
      "Germany --> PROPN\n",
      "LISTEN --> NOUN\n",
      ": --> PUNCT\n",
      "The --> DET\n",
      "extraordinary --> ADJ\n",
      "story --> NOUN\n",
      "of --> ADP\n",
      "Putin --> PROPN\n",
      "'s --> PART\n",
      "life --> NOUN\n",
      "A --> DET\n",
      "revealing --> VERB\n",
      "history --> NOUN\n",
      "as --> SCONJ\n",
      "told --> VERB\n",
      "by --> ADP\n",
      "those --> PRON\n",
      "who --> PRON\n",
      "have --> AUX\n",
      "watched --> VERB\n",
      ", --> PUNCT\n",
      "studied --> VERB\n",
      "and --> CCONJ\n",
      "worked --> VERB\n",
      "with --> ADP\n",
      "the --> DET\n",
      "Russian --> ADJ\n",
      "president --> NOUN\n",
      "© --> PROPN\n",
      "2022 --> NUM\n",
      "BBC --> PROPN\n",
      ". --> PUNCT\n",
      "The --> DET\n",
      "BBC --> PROPN\n",
      "is --> AUX\n",
      "not --> PART\n",
      "responsible --> ADJ\n",
      "for --> ADP\n",
      "the --> DET\n",
      "content --> NOUN\n",
      "of --> ADP\n",
      "external --> ADJ\n",
      "sites --> NOUN\n",
      ". --> PUNCT\n",
      "Read --> VERB\n",
      "about --> ADP\n",
      "our --> PRON\n",
      "approach --> NOUN\n",
      "to --> ADP\n",
      "external --> ADJ\n",
      "linking --> NOUN\n",
      ". --> PUNCT\n"
     ]
    }
   ],
   "source": [
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "# create a nlp object\n",
    "doc = nlp(text1)\n",
    "\n",
    "# tokenized\n",
    "for token in doc:\n",
    "    print(token.text, \"-->\", token.pos_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a82a115f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'proper noun'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain('PROPN')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "913ccdaa",
   "metadata": {},
   "source": [
    "### 2. 依存句法分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fc564521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This --> det\n",
      "video --> nsubjpass\n",
      "can --> aux\n",
      "not --> neg\n",
      "be --> auxpass\n",
      "played --> ROOT\n",
      "Ukrainian --> amod\n",
      "President --> compound\n",
      "Volodymyr --> compound\n",
      "Zelensky --> nsubj\n",
      "has --> aux\n",
      "accused --> conj\n",
      "Russia --> dobj\n",
      "of --> prep\n",
      "implementing --> pcomp\n",
      "\" --> punct\n",
      "a --> det\n",
      "bloody --> amod\n",
      "reconstruction --> dobj\n",
      "of --> prep\n",
      "Nazism --> pobj\n",
      "\" --> punct\n",
      ". --> punct\n",
      "In --> prep\n",
      "a --> det\n",
      "speech --> pobj\n",
      "commemorating --> acl\n",
      "World --> compound\n",
      "War --> compound\n",
      "Two --> dobj\n",
      ", --> punct\n",
      "he --> nsubj\n",
      "said --> ROOT\n",
      "the --> det\n",
      "Russian --> amod\n",
      "army --> nsubj\n",
      "was --> aux\n",
      "replicating --> ccomp\n",
      "Nazi --> nmod\n",
      "\" --> punct\n",
      "atrocities --> dobj\n",
      "\" --> punct\n",
      "during --> prep\n",
      "its --> poss\n",
      "invasion --> pobj\n",
      "of --> prep\n",
      "his --> poss\n",
      "country --> pobj\n",
      ". --> punct\n",
      "\" --> punct\n",
      "Darkness --> nsubj\n",
      "has --> aux\n",
      "returned --> ccomp\n",
      "to --> prep\n",
      "Ukraine --> pobj\n",
      "and --> cc\n",
      "it --> nsubj\n",
      "has --> aux\n",
      "become --> conj\n",
      "black --> acomp\n",
      "and --> cc\n",
      "white --> conj\n",
      "again --> advmod\n",
      ", --> punct\n",
      "\" --> punct\n",
      "he --> nsubj\n",
      "said --> ROOT\n",
      "in --> prep\n",
      "his --> poss\n",
      "video --> compound\n",
      "address --> pobj\n",
      ". --> punct\n",
      "  --> dep\n",
      "Footage --> nsubj\n",
      "in --> prep\n",
      "the --> det\n",
      "video --> pobj\n",
      "showed --> ROOT\n",
      "the --> det\n",
      "Ukrainian --> amod\n",
      "leader --> dobj\n",
      "against --> prep\n",
      "a --> det\n",
      "backdrop --> pobj\n",
      "of --> prep\n",
      "destroyed --> amod\n",
      "residential --> amod\n",
      "buildings --> pobj\n",
      ". --> punct\n",
      "\" --> punct\n",
      "Evil --> nsubj\n",
      "has --> aux\n",
      "returned --> ccomp\n",
      ", --> punct\n",
      "in --> prep\n",
      "a --> det\n",
      "different --> amod\n",
      "uniform --> pobj\n",
      ", --> punct\n",
      "under --> prep\n",
      "different --> amod\n",
      "slogans --> pobj\n",
      ", --> punct\n",
      "but --> cc\n",
      "for --> conj\n",
      "the --> det\n",
      "same --> amod\n",
      "purpose --> pobj\n",
      ", --> punct\n",
      "\" --> punct\n",
      "he --> nsubj\n",
      "added --> ROOT\n",
      ". --> punct\n",
      "  --> dep\n",
      "The --> det\n",
      "video --> nsubj\n",
      "also --> advmod\n",
      "featured --> ROOT\n",
      "archive --> compound\n",
      "footage --> dobj\n",
      "of --> prep\n",
      "World --> compound\n",
      "War --> nmod\n",
      "Two --> nummod\n",
      "and --> cc\n",
      "black --> conj\n",
      "- --> punct\n",
      "and --> cc\n",
      "- --> punct\n",
      "white --> conj\n",
      "footage --> pobj\n",
      "of --> prep\n",
      "Russia --> poss\n",
      "'s --> case\n",
      "invasion --> pobj\n",
      ". --> punct\n",
      "When --> advmod\n",
      "Ukraine --> nsubjpass\n",
      "was --> auxpass\n",
      "invaded --> advcl\n",
      "by --> agent\n",
      "Russia --> pobj\n",
      "in --> prep\n",
      "late --> amod\n",
      "February --> pobj\n",
      ", --> punct\n",
      "Moscow --> nsubj\n",
      "maintained --> ROOT\n",
      "its --> poss\n",
      "operation --> dobj\n",
      "was --> ccomp\n",
      "in --> prep\n",
      "part --> pobj\n",
      "to --> prep\n",
      "\" --> punct\n",
      "de --> pobj\n",
      "- --> pobj\n",
      "Nazify --> pobj\n",
      "\" --> punct\n",
      "the --> det\n",
      "country --> pobj\n",
      ". --> punct\n",
      "Russian --> amod\n",
      "President --> compound\n",
      "Vladimir --> compound\n",
      "Putin --> nsubj\n",
      "returned --> ROOT\n",
      "to --> prep\n",
      "the --> det\n",
      "theme --> pobj\n",
      "in --> prep\n",
      "his --> poss\n",
      "own --> amod\n",
      "address --> pobj\n",
      "congratulating --> acl\n",
      "former --> amod\n",
      "Soviet --> amod\n",
      "nations --> dobj\n",
      "on --> prep\n",
      "the --> det\n",
      "77th --> amod\n",
      "anniversary --> pobj\n",
      "of --> prep\n",
      "Nazi --> compound\n",
      "Germany --> poss\n",
      "'s --> case\n",
      "defeat --> pobj\n",
      ", --> punct\n",
      "saying --> advcl\n",
      "that --> mark\n",
      "\" --> punct\n",
      "as --> prep\n",
      "in --> prep\n",
      "1945 --> pobj\n",
      ", --> punct\n",
      "victory --> nsubj\n",
      "will --> aux\n",
      "be --> ccomp\n",
      "ours --> acomp\n",
      "\" --> punct\n",
      ". --> punct\n",
      "  --> dep\n",
      "Ukrainian --> amod\n",
      "officials --> nsubj\n",
      "fear --> ROOT\n",
      "Russia --> nsubj\n",
      "may --> aux\n",
      "step --> ccomp\n",
      "up --> prt\n",
      "its --> poss\n",
      "attacks --> dobj\n",
      "ahead --> advmod\n",
      "of --> prep\n",
      "Russia --> poss\n",
      "'s --> case\n",
      "Victory --> compound\n",
      "Day --> compound\n",
      "commemorations --> pobj\n",
      "on --> prep\n",
      "Monday --> pobj\n",
      ". --> punct\n",
      "Mr --> compound\n",
      "Zelensky --> nsubj\n",
      "appealed --> ROOT\n",
      "to --> prep\n",
      "European --> amod\n",
      "nations --> pobj\n",
      ", --> punct\n",
      "including --> prep\n",
      "the --> det\n",
      "UK --> pobj\n",
      ", --> punct\n",
      "France --> conj\n",
      "and --> cc\n",
      "the --> det\n",
      "Netherlands --> conj\n",
      ", --> punct\n",
      "by --> prep\n",
      "likening --> pcomp\n",
      "Nazi --> amod\n",
      "bombings --> dobj\n",
      "of --> prep\n",
      "their --> poss\n",
      "towns --> pobj\n",
      "and --> cc\n",
      "cities --> conj\n",
      "to --> prep\n",
      "Russian --> amod\n",
      "strikes --> pobj\n",
      "on --> prep\n",
      "urban --> amod\n",
      "areas --> pobj\n",
      "in --> prep\n",
      "Ukraine --> pobj\n",
      ". --> punct\n",
      "Meanwhile --> advmod\n",
      ", --> punct\n",
      "more --> amod\n",
      "than --> quantmod\n",
      "60 --> nummod\n",
      "people --> nsubjpass\n",
      "are --> auxpass\n",
      "feared --> ccomp\n",
      "dead --> oprd\n",
      "after --> mark\n",
      "a --> det\n",
      "Russian --> amod\n",
      "bomb --> nsubj\n",
      "hit --> advcl\n",
      "a --> det\n",
      "school --> dobj\n",
      "in --> prep\n",
      "eastern --> amod\n",
      "Ukraine --> pobj\n",
      "on --> prep\n",
      "Saturday --> pobj\n",
      ", --> punct\n",
      "local --> amod\n",
      "authorities --> nsubj\n",
      "said --> ROOT\n",
      ". --> punct\n",
      "  --> dep\n",
      "The --> det\n",
      "governor --> nsubj\n",
      "of --> prep\n",
      "Ukraine --> poss\n",
      "'s --> case\n",
      "Luhansk --> compound\n",
      "region --> pobj\n",
      ", --> punct\n",
      "Serhiy --> compound\n",
      "Haidai --> appos\n",
      ", --> punct\n",
      "said --> ROOT\n",
      "around --> advmod\n",
      "90 --> nummod\n",
      "local --> amod\n",
      "residents --> nsubj\n",
      "had --> aux\n",
      "been --> aux\n",
      "sheltering --> ccomp\n",
      "in --> prep\n",
      "the --> det\n",
      "school --> pobj\n",
      "in --> prep\n",
      "the --> det\n",
      "village --> pobj\n",
      "of --> prep\n",
      "Bilohorivka --> pobj\n",
      ", --> punct\n",
      "near --> prep\n",
      "the --> det\n",
      "frontline --> pobj\n",
      "in --> prep\n",
      "the --> det\n",
      "Donbas --> compound\n",
      "region --> pobj\n",
      ". --> punct\n",
      "  --> dep\n",
      "' --> punct\n",
      "If --> mark\n",
      "this --> nsubj\n",
      "is --> advcl\n",
      "true --> acomp\n",
      ", --> punct\n",
      "then --> advmod\n",
      "I --> nsubj\n",
      "am --> ROOT\n",
      "also --> advmod\n",
      "a --> det\n",
      "Nazi --> attr\n",
      "' --> punct\n",
      "' --> punct\n",
      "We --> nsubj\n",
      "tried --> ROOT\n",
      "not --> neg\n",
      "to --> aux\n",
      "watch --> xcomp\n",
      "' --> punct\n",
      ": --> punct\n",
      "Terror --> dobj\n",
      "in --> prep\n",
      "Russian --> npadvmod\n",
      "- --> punct\n",
      "occupied --> amod\n",
      "city --> compound\n",
      "Ukraine --> compound\n",
      "war --> pobj\n",
      ": --> punct\n",
      "Returning --> csubj\n",
      "to --> prep\n",
      "the --> det\n",
      "place --> pobj\n",
      "my --> poss\n",
      "father --> nsubjpass\n",
      "was --> auxpass\n",
      "killed --> relcl\n",
      "Sweden --> dobj\n",
      "confirms --> ccomp\n",
      "it --> nsubj\n",
      "will --> aux\n",
      "apply --> ccomp\n",
      "to --> aux\n",
      "join --> xcomp\n",
      "Nato --> dobj\n",
      "The --> det\n",
      "children --> poss\n",
      "'s --> case\n",
      "camp --> dobj\n",
      "that --> nsubj\n",
      "became --> relcl\n",
      "an --> det\n",
      "execution --> compound\n",
      "ground --> compound\n",
      "Buffalo --> compound\n",
      "gunman --> nsubj\n",
      "planned --> conj\n",
      "second --> amod\n",
      "attack --> dobj\n",
      ", --> punct\n",
      "police --> nsubj\n",
      "say --> ROOT\n",
      "What --> dobj\n",
      "Indigenous --> compound\n",
      "Australians --> nsubj\n",
      "want --> ccomp\n",
      "from --> prep\n",
      "the --> det\n",
      "election --> pobj\n",
      ". --> punct\n",
      "VideoWhat --> dep\n",
      "Indigenous --> compound\n",
      "Australians --> nsubj\n",
      "want --> ROOT\n",
      "from --> prep\n",
      "the --> det\n",
      "election --> pobj\n",
      "Pinned --> acl\n",
      "down --> prt\n",
      "by --> agent\n",
      "Russian --> amod\n",
      "fire --> pobj\n",
      "in --> prep\n",
      "key --> amod\n",
      "frontline --> compound\n",
      "village --> pobj\n",
      "Why --> advmod\n",
      "Urdu --> compound\n",
      "language --> nsubj\n",
      "draws --> ccomp\n",
      "ire --> dobj\n",
      "of --> prep\n",
      "India --> pobj\n",
      "’s --> case\n",
      "right --> amod\n",
      "- --> punct\n",
      "wing --> nmod\n",
      "The --> det\n",
      "children --> poss\n",
      "'s --> case\n",
      "camp --> pobj\n",
      "that --> nsubj\n",
      "became --> relcl\n",
      "an --> det\n",
      "execution --> compound\n",
      "ground --> attr\n",
      "Can --> aux\n",
      "Africa --> nsubj\n",
      "replace --> advcl\n",
      "Russian --> amod\n",
      "gas --> compound\n",
      "supplies --> dobj\n",
      "to --> prep\n",
      "Europe --> pobj\n",
      "? --> punct\n",
      "How --> advmod\n",
      "Australia --> nsubj\n",
      "became --> ROOT\n",
      "the --> det\n",
      "anxious --> amod\n",
      "country --> attr\n",
      "The --> det\n",
      "new --> amod\n",
      "jet --> compound\n",
      "set --> npadvmod\n",
      "- --> punct\n",
      "why --> advmod\n",
      "private --> amod\n",
      "plane --> compound\n",
      "use --> nsubj\n",
      "has --> aux\n",
      "soared --> relcl\n",
      "A --> det\n",
      "tale --> appos\n",
      "of --> prep\n",
      "two --> nummod\n",
      "women --> poss\n",
      "’s --> case\n",
      "murders --> pobj\n",
      "– --> punct\n",
      "and --> cc\n",
      "media --> compound\n",
      "bias --> conj\n",
      "What --> nsubj\n",
      "’s --> nsubj\n",
      "the --> det\n",
      "difference --> dobj\n",
      "between --> prep\n",
      "love --> pobj\n",
      "and --> cc\n",
      "lust --> conj\n",
      "? --> punct\n",
      "Russia --> nsubj\n",
      "pushed --> ROOT\n",
      "back --> advmod\n",
      "from --> prep\n",
      "Kharkiv --> amod\n",
      "- --> punct\n",
      "front --> amod\n",
      "line --> compound\n",
      "report --> compound\n",
      "Correspondent --> compound\n",
      "Quentin --> compound\n",
      "Sommerville --> pobj\n",
      "and --> cc\n",
      "cameraman --> compound\n",
      "Darren --> compound\n",
      "Conway --> conj\n",
      "are --> advcl\n",
      "with --> prep\n",
      "Ukrainian --> amod\n",
      "troops --> pobj\n",
      "as --> mark\n",
      "they --> nsubj\n",
      "advance --> advcl\n",
      ". --> punct\n",
      "Russia --> ROOT\n",
      "fighting --> acl\n",
      "for --> prep\n",
      "motherland --> pobj\n",
      "in --> prep\n",
      "Ukraine --> compound\n",
      "- --> punct\n",
      "Putin --> pobj\n",
      "The --> det\n",
      "Russian --> amod\n",
      "leader --> nsubj\n",
      "uses --> ROOT\n",
      "Victory --> compound\n",
      "Day --> dobj\n",
      "to --> aux\n",
      "tie --> xcomp\n",
      "the --> det\n",
      "war --> dobj\n",
      "in --> prep\n",
      "Ukraine --> pobj\n",
      "to --> prep\n",
      "the --> det\n",
      "fight --> pobj\n",
      "against --> prep\n",
      "Nazi --> compound\n",
      "Germany --> compound\n",
      "LISTEN --> pobj\n",
      ": --> punct\n",
      "The --> det\n",
      "extraordinary --> amod\n",
      "story --> nsubj\n",
      "of --> prep\n",
      "Putin --> poss\n",
      "'s --> case\n",
      "life --> pobj\n",
      "A --> det\n",
      "revealing --> amod\n",
      "history --> appos\n",
      "as --> mark\n",
      "told --> advcl\n",
      "by --> agent\n",
      "those --> pobj\n",
      "who --> nsubj\n",
      "have --> aux\n",
      "watched --> relcl\n",
      ", --> punct\n",
      "studied --> ROOT\n",
      "and --> cc\n",
      "worked --> conj\n",
      "with --> prep\n",
      "the --> det\n",
      "Russian --> amod\n",
      "president --> pobj\n",
      "© --> conj\n",
      "2022 --> nummod\n",
      "BBC --> npadvmod\n",
      ". --> punct\n",
      "The --> det\n",
      "BBC --> nsubj\n",
      "is --> ROOT\n",
      "not --> neg\n",
      "responsible --> acomp\n",
      "for --> prep\n",
      "the --> det\n",
      "content --> pobj\n",
      "of --> prep\n",
      "external --> amod\n",
      "sites --> pobj\n",
      ". --> punct\n",
      "Read --> ROOT\n",
      "about --> prep\n",
      "our --> poss\n",
      "approach --> dobj\n",
      "to --> prep\n",
      "external --> amod\n",
      "linking --> pobj\n",
      ". --> punct\n"
     ]
    }
   ],
   "source": [
    "for token in doc:\n",
    "    print(token.text, \"-->\", token.dep_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f926625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adjectival modifier | direct object | nominal subject (passive)\n"
     ]
    }
   ],
   "source": [
    "print(spacy.explain('amod'),\"|\", spacy.explain('dobj'),\"|\",spacy.explain('nsubjpass'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae56be6",
   "metadata": {},
   "source": [
    "### 3. NER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3fa853b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "doc = nlp(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8ddb52f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ukrainian NORP\n",
      "Volodymyr Zelensky PERSON\n",
      "Russia GPE\n",
      "Nazism NORP\n",
      "World War Two EVENT\n",
      "Russian NORP\n",
      "Nazi NORP\n",
      "Ukraine GPE\n",
      "Ukrainian NORP\n",
      "World War Two EVENT\n",
      "Russia GPE\n",
      "Ukraine GPE\n",
      "Russia GPE\n",
      "late February DATE\n",
      "Moscow GPE\n",
      "Russian NORP\n",
      "Vladimir Putin PERSON\n",
      "Soviet NORP\n",
      "the 77th anniversary DATE\n",
      "Nazi Germany's GPE\n",
      "1945 DATE\n",
      "Russia GPE\n",
      "Russia GPE\n",
      "Monday DATE\n",
      "Zelensky PERSON\n",
      "European NORP\n",
      "UK GPE\n",
      "France GPE\n",
      "Netherlands GPE\n",
      "Nazi NORP\n",
      "Russian NORP\n",
      "Ukraine GPE\n",
      "more than 60 CARDINAL\n",
      "Russian NORP\n",
      "Ukraine GPE\n",
      "Saturday DATE\n",
      "Ukraine GPE\n",
      "Luhansk GPE\n",
      "Serhiy Haidai PERSON\n",
      "around 90 CARDINAL\n",
      "Bilohorivka GPE\n",
      "Donbas GPE\n",
      "Nazi ORG\n",
      "Russian NORP\n",
      "Ukraine GPE\n",
      "Sweden GPE\n",
      "Nato ORG\n",
      "Buffalo ORG\n",
      "second ORDINAL\n",
      "Australians NORP\n",
      "Russian NORP\n",
      "Urdu GPE\n",
      "India GPE\n",
      "Africa LOC\n",
      "Russian NORP\n",
      "Europe LOC\n",
      "Australia GPE\n",
      "two CARDINAL\n",
      "Russia GPE\n",
      "Quentin Sommerville PERSON\n",
      "Darren Conway PERSON\n",
      "Ukrainian NORP\n",
      "Russia GPE\n",
      "Ukraine - Putin GPE\n",
      "Russian NORP\n",
      "Ukraine GPE\n",
      "Nazi NORP\n",
      "Germany GPE\n",
      "Putin PERSON\n",
      "Russian NORP\n",
      "2022 ORG\n",
      "BBC ORG\n",
      "BBC ORG\n"
     ]
    }
   ],
   "source": [
    "for entity in doc.ents:\n",
    "    print(entity.text, entity.label_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a4c35c",
   "metadata": {},
   "source": [
    "### 4. Spacy 匹配"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0bbcae84",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en_core_web_sm')\n",
    "from spacy.matcher import Matcher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89c812aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用 spacy詞彙表初始化 matcher\n",
    "matcher = Matcher(nlp.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2f0159ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7d9f9148",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the rules\n",
    "pattern = [{'TEXT': 'Ukraine'}, {'TEXT':'war'}, {'POS':'NOUN'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0819a9b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new rules\n",
    "matcher.add('rule_1', [pattern])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7767ba84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = matcher(doc); matches\n",
    "# 匹配ID, 標記位置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72ddd1d",
   "metadata": {},
   "source": [
    "**目標是提取'lemon water'這個短語**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7cfbce57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for match_id, start, end in matches:\n",
    "    matched_span = doc[start:end]\n",
    "    print(matched_span.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6323cb8",
   "metadata": {},
   "source": [
    "## Tqdm basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6eae5c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from time import sleep\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "be9e257c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.21it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(10)):\n",
    "    sleep(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "808c64ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.06it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in trange(100):\n",
    "    sleep(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "63a0bce2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process d: 100%|█████████████████████████████████████████████████████████████████████████| 4/4 [00:04<00:00,  1.01s/it]\n"
     ]
    }
   ],
   "source": [
    "pbar = tqdm(['a','b','c','d'])\n",
    "for char in pbar:\n",
    "    pbar.set_description(f\"Process {char}\")\n",
    "    sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c198a10",
   "metadata": {},
   "source": [
    "## Gensim Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c1c2d8",
   "metadata": {},
   "source": [
    "There are two types of input you can feed into Word2Vec.\n",
    "- 1D list: [ ' My name is Megan ' ,  ' Nice to meet you. ' ]\n",
    "> [ ' M ' , ' y ' , ' n ' , ' a ' ... ]\n",
    "- 2D list: [ [ ' My name is Megan ' ] ,  [ ' Nice to meet you. ' ] ]\n",
    "> [ ' My ' , ' name ' , ' is ' ... ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "84913c0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gensim in c:\\users\\megan\\python\\lib\\site-packages (4.2.0)\n",
      "Requirement already satisfied: Cython==0.29.28 in c:\\users\\megan\\python\\lib\\site-packages (from gensim) (0.29.28)\n",
      "Requirement already satisfied: scipy>=0.18.1 in c:\\users\\megan\\python\\lib\\site-packages (from gensim) (1.6.2)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\megan\\python\\lib\\site-packages (from gensim) (5.2.1)\n",
      "Requirement already satisfied: numpy>=1.17.0 in c:\\users\\megan\\python\\lib\\site-packages (from gensim) (1.20.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "449a370c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.word2vec import Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bc98d283",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.test.utils import common_texts\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "# initialize a model\n",
    "sentences = [nltk.wordpunct_tokenize(word) for word in nltk.sent_tokenize(text1)]\n",
    "model = Word2Vec(sentences=sentences, vector_size=100, window=5, min_count=1, workers=4)\n",
    "model.save('word2vec.model')\n",
    "\n",
    "# Note that sentences must be restartable, not just a generator, to allow the algorithm to stream over your data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b7608d35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "This video can not be played Ukrainian President Volodymyr Zelensky has accused Russia of implementing \"a bloody reconstruction of Nazism\". In a speech commemorating World War Two, he said the Russian army was replicating Nazi \"atrocities\" during its invasion of his country. \"Darkness has returned to Ukraine and it has become black and white again,\" he said in his video address.  Footage in the video showed the Ukrainian leader against a backdrop of destroyed residential buildings. \"Evil has returned, in a different uniform, under different slogans, but for the same purpose,\" he added.  The video also featured archive footage of World War Two and black-and-white footage of Russia's invasion. When Ukraine was invaded by Russia in late February, Moscow maintained its operation was in part to \"de-Nazify\" the country. Russian President Vladimir Putin returned to the theme in his own address congratulating former Soviet nations on the 77th anniversary of Nazi Germany's defeat, saying that \"as in 1945, victory will be ours\".  Ukrainian officials fear Russia may step up its attacks ahead of Russia's Victory Day commemorations on Monday. Mr Zelensky appealed to European nations, including the UK, France and the Netherlands, by likening Nazi bombings of their towns and cities to Russian strikes on urban areas in Ukraine. Meanwhile, more than 60 people are feared dead after a Russian bomb hit a school in eastern Ukraine on Saturday, local authorities said.  The governor of Ukraine's Luhansk region, Serhiy Haidai, said around 90 local residents had been sheltering in the school in the village of Bilohorivka, near the frontline in the Donbas region.  'If this is true, then I am also a Nazi' 'We tried not to watch': Terror in Russian-occupied city Ukraine war: Returning to the place my father was killed Sweden confirms it will apply to join Nato The children's camp that became an execution ground Buffalo gunman planned second attack, police say What Indigenous Australians want from the election. VideoWhat Indigenous Australians want from the election Pinned down by Russian fire in key frontline village Why Urdu language draws ire of India’s right-wing The children's camp that became an execution ground Can Africa replace Russian gas supplies to Europe? How Australia became the anxious country The new jet set - why private plane use has soared A tale of two women’s murders – and media bias What’s the difference between love and lust? Russia pushed back from Kharkiv - front line report Correspondent Quentin Sommerville and cameraman Darren Conway are with Ukrainian troops as they advance. Russia fighting for motherland in Ukraine - Putin The Russian leader uses Victory Day to tie the war in Ukraine to the fight against Nazi Germany LISTEN: The extraordinary story of Putin's life A revealing history as told by those who have watched, studied and worked with the Russian president © 2022 BBC. The BBC is not responsible for the content of external sites. Read about our approach to external linking."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aa62cf8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Word2Vec.load('word2vec.model')\n",
    "model.train([['hello', 'world'], ['I love you']], total_examples=1, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2ffd8322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get numpy word vector\n",
    "# the trained word vectors are stored in a KeyedVectors instance (model.wv)\n",
    "vector = model.wv['fear']\n",
    "\n",
    "# get another similar words\n",
    "similarities = model.wv.most_similar('fear', topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "991430f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Quentin', 0.26901254057884216),\n",
       " ('life', 0.2582971453666687),\n",
       " ('ours', 0.2448005974292755),\n",
       " ('also', 0.23192386329174042),\n",
       " ('residential', 0.2312617301940918),\n",
       " ('Sommerville', 0.20403794944286346),\n",
       " ('defeat', 0.18168693780899048),\n",
       " ('Europe', 0.18113252520561218),\n",
       " ('leader', 0.1755145639181137),\n",
       " ('appealed', 0.16737128794193268)]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a76b5943",
   "metadata": {},
   "source": [
    "**The reason for seperating the trained vectors into *KeyedVectors* is that if you don't need to continue training, the model state can be discarded, keeping just the vectors and their keys proper.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0e325db4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00245672,  0.00603815, -0.00967525, -0.00141643,  0.00512494,\n",
       "        0.00709658,  0.00687273,  0.00267384,  0.00682325, -0.00888664,\n",
       "        0.00534703, -0.00254393,  0.00373437, -0.00959953, -0.00903777,\n",
       "        0.00498094, -0.00116856,  0.007398  , -0.00933765,  0.0098441 ,\n",
       "       -0.00597251,  0.00053171,  0.00463508, -0.00218624,  0.0073464 ,\n",
       "        0.00049369, -0.00298038, -0.00118586, -0.00948384, -0.00415619,\n",
       "       -0.00393587,  0.00825014, -0.00999267, -0.00215082,  0.0073504 ,\n",
       "        0.00794078, -0.00848447,  0.00126483, -0.00933387,  0.00684145,\n",
       "        0.0095866 ,  0.00327328, -0.00941152, -0.00059756, -0.00174051,\n",
       "        0.00432746,  0.00696998,  0.0044567 , -0.00923279,  0.00738247,\n",
       "        0.0027651 , -0.00080843, -0.00289296,  0.0006086 , -0.00568433,\n",
       "        0.00530166, -0.00903948,  0.00029373,  0.00228392,  0.00245247,\n",
       "        0.00908578, -0.00528315,  0.00965737,  0.00946033, -0.00720558,\n",
       "        0.00274826,  0.00971554,  0.00304767, -0.00223885,  0.00495838,\n",
       "        0.00485784,  0.00580927, -0.00331282, -0.00809757, -0.00161779,\n",
       "       -0.00256334, -0.00319469,  0.00197334, -0.00967193,  0.00990002,\n",
       "       -0.00177387,  0.00033833,  0.0043923 , -0.00991163,  0.00428862,\n",
       "        0.00939496,  0.0088644 ,  0.00105877, -0.00206867, -0.00871348,\n",
       "        0.00629289, -0.0030743 ,  0.00864768,  0.00919024, -0.00761545,\n",
       "       -0.00335859,  0.00532921,  0.00111422, -0.00239552, -0.0034877 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "\n",
    "# store just the words + their trained embeddings.\n",
    "word_vect = model.wv\n",
    "word_vect.save('word2vec.wordvectors')\n",
    "\n",
    "# Load back with memory-mapping, shared across processes\n",
    "wv = KeyedVectors.load('word2vec.wordvectors', mmap='r')\n",
    "vector = wv['fear'] # get the numpy vector of \"fear\"\n",
    "\n",
    "vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "167c2a3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2bacea",
   "metadata": {},
   "source": [
    "## Skip-gram and CBOW"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2515c8d0",
   "metadata": {},
   "source": [
    "**Word2Vec 提出兩種演算法。\n",
    "- Skip-gram\n",
    "\n",
    "利用中心詞來預測上下文。假定「柯文哲」為model input，輸出為固定的window長度的上下文詞彙，例如台北、市長、選舉...\n",
    "> negative sampling: 與其提供大量非上下文詞彙提供模型訓練，不如挑選少樣負面案例\n",
    "- CBOW\n",
    "\n",
    "利用上下文來預測中心詞，「台北」、「市長」、「參加」為model input -> 中心詞: 柯文哲\n",
    "> heirarchical softmax: 快速縮短訓練時間，將輸出層利用huffman coding建立二元樹，時間複雜度從O(n)降到O(log(n))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a043fd25",
   "metadata": {},
   "source": [
    "## PCA - Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d6dc64",
   "metadata": {},
   "source": [
    "**PCA是一種非監督式的學習，主要用於降維，可以用來過濾雜訊、特徵擷取等等。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d54be53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f146dc31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(1); rng\n",
    "X = np.dot(rng.rand(2,2), rng.randn(2,200)).T\n",
    "plt.scatter(X[:,0], X[:,1]);\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17971c40",
   "metadata": {},
   "source": [
    "**PCA unsupervised learning 是學習X-axis 及y-axis的關係，並非由X軸的資料去預測y軸的資料。**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c63b06",
   "metadata": {},
   "source": [
    ">Linear dimensionality reduction using `Singular Value Decomposition` of the\n",
    "data to project it to a lower dimensional space. The input data is centered\n",
    "but not scaled for each feature before applying the SVD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "333e6d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bb063643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance ratio:  [0.97634101 0.02365899] \n",
      "singular value:  [12.31843207  1.91757691]\n"
     ]
    }
   ],
   "source": [
    "print('variance ratio: ', pca.explained_variance_ratio_,\n",
    "      '\\nsingular value: ', pca.singular_values_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15291a72",
   "metadata": {},
   "source": [
    "# Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4c894bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if lemmatize == True: lemmatize the sentence and then store it to text_preprocessing\n",
    "# if pos == Ture: add text only if the name entity is in the pos selected list.\n",
    "\n",
    "def spacy_process(text, lemma=True, pos=True, pos_select=['VERB', 'NOUN', 'ADV', 'PART']):\n",
    "    text = re.sub('\\n', ' ', text)\n",
    "    text = re.sub('\\'s', ' ', text)\n",
    "    \n",
    "    nlp = spacy.load('en_core_web_sm')\n",
    "    nlp.disable_pipes('parser', 'ner')\n",
    "    \n",
    "    doc = nlp(text)\n",
    "    \n",
    "    if pos == False:\n",
    "        if lemma == True:\n",
    "            text_preprocessing = \" \".join([token.lemma_ for token in doc])\n",
    "        if lemma == False:\n",
    "            text_preprocessing = \" \".join([token.text for token in doc])\n",
    "    elif pos == True:\n",
    "        if lemma == True:\n",
    "            text_preprocessing = \" \".join([token.lemma_ for token in doc if token.pos_ in pos_select])\n",
    "        if lemma == False:\n",
    "            text_preprocessing = \" \".join([token.text for token in doc if token.pos_ in pos_select])\n",
    "           \n",
    "    # text_preprocessing is now including text stemmed from the specified pos_select.\n",
    "    return text_preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a9af9dc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'video not play accuse implement reconstruction speech commemorate say army replicate atrocity invasion country darkness return become again say video address video show leader backdrop destroy building evil return uniform slogan purpose add video also feature archive footage footage invasion invade maintain operation part nazify country return theme address congratulate nation anniversary defeat say victory official fear step attack ahead commemoration appeal nation include liken bombing town city strike area meanwhile people fear bomb hit school authority say governor region say around resident shelter school village frontline region then also try not to watch occupy city war return place father kill confirm apply to join child camp become execution ground gunman plan attack police say want election videowhat want election pin fire frontline village language draw ire ’s wing child camp become execution ground replace gas supply become country jet set plane use soar tale woman ’s murder medium bias ’ difference love lust push back line report troop advance fight motherland leader use to tie war fight LISTEN story life reveal history tell watch study work president not content site read approach linking'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy_process(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e8b6dc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e59fddfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b19ed47c46724e7faaa5dc93f3e3c653",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<tqdm.notebook.tqdm_notebook at 0x2cbbd420400>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tqdm(amazon_reviewDF.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9ee7eb69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File already exists.\n"
     ]
    }
   ],
   "source": [
    "destination_path = './Amazon review cleaned.csv'\n",
    "\n",
    "if not os.path.isfile(destination_path):\n",
    "    cleaned_review_data = amazon_reviewDF\n",
    "    \n",
    "    for index in tqdm(amazon_reviewDF.index):\n",
    "        sentence = amazon_reviewDF.text[index]\n",
    "        # clean\n",
    "        cleaned_sentence = spacy_process(sentence, pos_select=['VERB', \"ADJ\", \"ADV\"])\n",
    "        cleaned_review_data.text[index] = cleaned_sentence\n",
    "    \n",
    "    cleaned_review_data.to_csv(destination_path, index=False)\n",
    "else:\n",
    "    print('File already exists.');\n",
    "    cleaned_review_data = pd.read_csv(destination_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73629da9",
   "metadata": {},
   "source": [
    "**Now I have 2 data set:**\n",
    "1. the original data set: amazon_reviewDF\n",
    "2. After spacy processing: cleaned_review_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f7efd11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text     0\n",
       "label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing na\n",
    "cleaned_review_data = pd.read_csv(destination_path, delimiter=',')\n",
    "cleaned_review_data.isna().sum()\n",
    "cleaned_review_data.dropna(inplace=True)\n",
    "cleaned_review_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "93a3f270",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# splitting training and testing data for validation\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(cleaned_review_data.text,\n",
    "                                                    cleaned_review_data.label, \n",
    "                                                    test_size=0.3, \n",
    "                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b6789641",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# encode the target variable (pos, neg) to be (1, 0)\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "train_y_en = encoder.fit_transform(train_y)\n",
    "test_y_en = encoder.fit_transform(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c14fb790",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# word level tf-idf\n",
    "tfidf_vect = TfidfVectorizer(stop_words='english', max_features=None)\n",
    "\n",
    "    # learn vocab and idf from the training set\n",
    "tfidf_vect.fit(train_x.values.astype('U'))\n",
    "\n",
    "    # transfer into dtpyr: <U400\n",
    "xtrain_tfidf = tfidf_vect.transform(train_x.values.astype('U'))\n",
    "xtest_tfidf = tfidf_vect.transform(test_x.values.astype('U'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "48370dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6992, 6103), (2997, 6103), 6992, 2997)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain_tfidf.shape, xtest_tfidf.shape, len(train_y_en), len(test_y_en)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff68233",
   "metadata": {},
   "source": [
    "**Try to use Gensim**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dc743b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "import multiprocessing\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5b0e5476",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num of cpu: 8\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import Word2Vec\n",
    "from gensim.models.word2vec import LineSentence\n",
    "\n",
    "workers = multiprocessing.cpu_count()\n",
    "print(f'num of cpu: {workers}')\n",
    "assert gensim.models.doc2vec.FAST_VERSION > -1, \"This will be painfully slow otherwise.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "10dfc7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['cheap',\n",
       "  'buy',\n",
       "  'cheap',\n",
       "  'lose',\n",
       "  'simple',\n",
       "  'enough',\n",
       "  'together',\n",
       "  'want',\n",
       "  'play',\n",
       "  'very',\n",
       "  'quick',\n",
       "  'top',\n",
       "  'few',\n",
       "  'think',\n",
       "  'too',\n",
       "  'rough',\n",
       "  'back'],\n",
       " ['real', 'receive', 'respond', 'quickly', 'tell', 'sell', 'fraudulent'],\n",
       " ['poor',\n",
       "  'poorly',\n",
       "  'make',\n",
       "  'consist',\n",
       "  'low',\n",
       "  'flimsy',\n",
       "  'assemble',\n",
       "  'go',\n",
       "  'come',\n",
       "  'apart',\n",
       "  'require',\n",
       "  'considerable',\n",
       "  'safely',\n",
       "  'effectively',\n",
       "  'outrageously',\n",
       "  'overprice',\n",
       "  'too',\n",
       "  'unfortunate',\n",
       "  'legendary',\n",
       "  'original',\n",
       "  'perhaps',\n",
       "  'handy',\n",
       "  'ever',\n",
       "  'manage',\n",
       "  'though',\n",
       "  'get',\n",
       "  'even'],\n",
       " ['favorite',\n",
       "  'other',\n",
       "  'full',\n",
       "  'get',\n",
       "  'together',\n",
       "  'lend',\n",
       "  'amazing',\n",
       "  'great',\n",
       "  'fair',\n",
       "  'other',\n",
       "  'get',\n",
       "  'lose',\n",
       "  'true'],\n",
       " ['as', 'well', 'go', 'simply', 'fall', 'get', 'basically', 'waste'],\n",
       " ['original', 'cheap', 'make', 'less', 'scary', 'well'],\n",
       " ['disgusting',\n",
       "  'first',\n",
       "  'major',\n",
       "  'full',\n",
       "  'so',\n",
       "  'great',\n",
       "  'garlic',\n",
       "  'afterwards',\n",
       "  'so',\n",
       "  'necessary',\n",
       "  'add',\n",
       "  'other',\n",
       "  'more',\n",
       "  'enough'],\n",
       " ['save',\n",
       "  'so',\n",
       "  'keep',\n",
       "  'over',\n",
       "  'again',\n",
       "  'get',\n",
       "  'right',\n",
       "  'punkish',\n",
       "  'give',\n",
       "  'too',\n",
       "  'much',\n",
       "  'somehow',\n",
       "  'end',\n",
       "  'solve',\n",
       "  'several',\n",
       "  'crazy',\n",
       "  'own',\n",
       "  'seemingly',\n",
       "  'average',\n",
       "  'soon',\n",
       "  'complicated'],\n",
       " ['great',\n",
       "  'forget',\n",
       "  'bring',\n",
       "  'back',\n",
       "  'think',\n",
       "  'good',\n",
       "  'enough',\n",
       "  'outstanding',\n",
       "  'same',\n",
       "  'ago',\n",
       "  'couple',\n",
       "  'modern',\n",
       "  'expect',\n",
       "  'first',\n",
       "  'see',\n",
       "  'mindless'],\n",
       " ['moral',\n",
       "  'difficult',\n",
       "  'explain',\n",
       "  'old',\n",
       "  'difficult',\n",
       "  'read',\n",
       "  'read',\n",
       "  'find',\n",
       "  'very',\n",
       "  'helpful',\n",
       "  'bring',\n",
       "  'moral',\n",
       "  'present',\n",
       "  'find',\n",
       "  'great',\n",
       "  'moral',\n",
       "  'give',\n",
       "  'difficult',\n",
       "  'current',\n",
       "  'morally',\n",
       "  'challenge',\n",
       "  'buy',\n",
       "  'young',\n",
       "  'often'],\n",
       " ['horrible',\n",
       "  'acceptable',\n",
       "  'excellent',\n",
       "  'rely',\n",
       "  'way',\n",
       "  'way',\n",
       "  'too',\n",
       "  'heavily',\n",
       "  'special',\n",
       "  'watch',\n",
       "  'original',\n",
       "  'very',\n",
       "  'little',\n",
       "  'special',\n",
       "  'perfect',\n",
       "  'get',\n",
       "  'then',\n",
       "  'play',\n",
       "  'straight',\n",
       "  'just',\n",
       "  'special',\n",
       "  'try',\n",
       "  'put',\n",
       "  'especially',\n",
       "  'original',\n",
       "  'perfect'],\n",
       " ['ridiculous',\n",
       "  'find',\n",
       "  'hard',\n",
       "  'believe',\n",
       "  'really',\n",
       "  'use',\n",
       "  'same',\n",
       "  'seriously'],\n",
       " ['flammable',\n",
       "  'low',\n",
       "  'good',\n",
       "  'fine',\n",
       "  'occasionally',\n",
       "  'clean',\n",
       "  'cold',\n",
       "  'black',\n",
       "  'then',\n",
       "  'catch'],\n",
       " ['think',\n",
       "  'so',\n",
       "  'horrible',\n",
       "  'german',\n",
       "  'really',\n",
       "  'bad',\n",
       "  'become',\n",
       "  'obsessed',\n",
       "  'get',\n",
       "  'even',\n",
       "  'most',\n",
       "  'superior'],\n",
       " ['perfectly',\n",
       "  'difficult',\n",
       "  'buy',\n",
       "  'original',\n",
       "  'permanently',\n",
       "  'fasten',\n",
       "  'then',\n",
       "  'drill',\n",
       "  'fasten',\n",
       "  'unfortunately',\n",
       "  'use',\n",
       "  'same',\n",
       "  'indicate',\n",
       "  'make',\n",
       "  'perfectly'],\n",
       " ['unnecessarily',\n",
       "  'dense',\n",
       "  'useful',\n",
       "  'useful',\n",
       "  'interesting',\n",
       "  'complete',\n",
       "  'too',\n",
       "  'great',\n",
       "  'general',\n",
       "  'well',\n",
       "  'worth',\n",
       "  'only',\n",
       "  'worth',\n",
       "  'say',\n",
       "  'worth',\n",
       "  'say',\n",
       "  'need',\n",
       "  'also',\n",
       "  'confuse',\n",
       "  'feel',\n",
       "  'more',\n",
       "  'willfully',\n",
       "  'complicate',\n",
       "  'ensure',\n",
       "  'more',\n",
       "  'again',\n",
       "  'worth',\n",
       "  'valid',\n",
       "  'useful',\n",
       "  'enough',\n",
       "  'extra',\n",
       "  'own'],\n",
       " ['hate', 'new', 'realize', 'turn', 'sure', 'give'],\n",
       " ['engulf',\n",
       "  'tremendous',\n",
       "  'feel',\n",
       "  'totally',\n",
       "  'engulf',\n",
       "  'put',\n",
       "  'great',\n",
       "  'really',\n",
       "  'feel',\n",
       "  'also',\n",
       "  'give',\n",
       "  'much',\n",
       "  'there',\n",
       "  'definite',\n",
       "  'interested',\n",
       "  'also',\n",
       "  'agreatly',\n",
       "  'write',\n",
       "  'most',\n",
       "  'important'],\n",
       " ['enjoyable',\n",
       "  'talented',\n",
       "  'varied',\n",
       "  'go',\n",
       "  'classical',\n",
       "  'popular',\n",
       "  'very',\n",
       "  'enjoyable'],\n",
       " ['great',\n",
       "  'work',\n",
       "  'great',\n",
       "  'use',\n",
       "  'old',\n",
       "  'work',\n",
       "  'right',\n",
       "  'little',\n",
       "  'long',\n",
       "  'replace',\n",
       "  'never',\n",
       "  'get',\n",
       "  'work',\n",
       "  'right'],\n",
       " ['squeeze',\n",
       "  'communicate',\n",
       "  'more',\n",
       "  'write',\n",
       "  'however',\n",
       "  'write',\n",
       "  'legible',\n",
       "  'little',\n",
       "  'hard',\n",
       "  'old',\n",
       "  'now',\n",
       "  'imagine',\n",
       "  'tremble'],\n",
       " ['guess',\n",
       "  'find',\n",
       "  'memorable',\n",
       "  'even',\n",
       "  'remember',\n",
       "  'sound',\n",
       "  'particular',\n",
       "  'cry',\n",
       "  'overall',\n",
       "  'impress'],\n",
       " ['fantastic',\n",
       "  'generate',\n",
       "  'invaluable',\n",
       "  'instantly',\n",
       "  'usable',\n",
       "  'great',\n",
       "  'never',\n",
       "  'see',\n",
       "  'right',\n",
       "  'wait'],\n",
       " ['terrible',\n",
       "  'terrible',\n",
       "  'terrible',\n",
       "  'quite',\n",
       "  'frankly',\n",
       "  'more',\n",
       "  'go',\n",
       "  'terrible',\n",
       "  'terrible'],\n",
       " ['break',\n",
       "  'right',\n",
       "  'first',\n",
       "  'close',\n",
       "  'even',\n",
       "  'push',\n",
       "  'few',\n",
       "  'come',\n",
       "  'back',\n",
       "  'out',\n",
       "  'cheaply',\n",
       "  'buy'],\n",
       " ['read',\n",
       "  'several',\n",
       "  'academic',\n",
       "  'still',\n",
       "  'read',\n",
       "  'give',\n",
       "  'effective',\n",
       "  'clearly',\n",
       "  'help',\n",
       "  'understand',\n",
       "  'once',\n",
       "  'really',\n",
       "  'understand',\n",
       "  'able'],\n",
       " ['say',\n",
       "  'long',\n",
       "  'listen',\n",
       "  'anti',\n",
       "  '-',\n",
       "  'working',\n",
       "  'profess',\n",
       "  'take',\n",
       "  'write',\n",
       "  'guess',\n",
       "  'easy',\n",
       "  'common',\n",
       "  'successful',\n",
       "  'somewhere',\n",
       "  'else'],\n",
       " ['bad',\n",
       "  'say',\n",
       "  'bad',\n",
       "  'ever',\n",
       "  'write',\n",
       "  'just',\n",
       "  'think',\n",
       "  'entertaining',\n",
       "  'go',\n",
       "  'rent',\n",
       "  'far',\n",
       "  'well',\n",
       "  'sorry',\n",
       "  'see',\n",
       "  'write'],\n",
       " ['buy',\n",
       "  'unfortunate',\n",
       "  'ever',\n",
       "  'speak',\n",
       "  'intelligent',\n",
       "  'enough',\n",
       "  'careful',\n",
       "  'now',\n",
       "  'new',\n",
       "  'due',\n",
       "  'more',\n",
       "  'old',\n",
       "  'buy',\n",
       "  'new'],\n",
       " ['emotional',\n",
       "  'intricately',\n",
       "  'allow',\n",
       "  'absorb',\n",
       "  'feel',\n",
       "  'refreshing',\n",
       "  'so',\n",
       "  'many',\n",
       "  'seem',\n",
       "  'try',\n",
       "  'too',\n",
       "  'hard',\n",
       "  'emotional'],\n",
       " ['write',\n",
       "  'few',\n",
       "  'actually',\n",
       "  'finish',\n",
       "  'create',\n",
       "  'take',\n",
       "  'read',\n",
       "  'most',\n",
       "  'hate',\n",
       "  'usual',\n",
       "  'amazing',\n",
       "  'write',\n",
       "  'slow',\n",
       "  'circular',\n",
       "  'straight',\n",
       "  'represent',\n",
       "  'think',\n",
       "  'many',\n",
       "  'remind',\n",
       "  'historical',\n",
       "  'psychological',\n",
       "  'little',\n",
       "  'keep',\n",
       "  'light',\n",
       "  'hearted',\n",
       "  'probably',\n",
       "  'mis',\n",
       "  'direct',\n",
       "  'purposely',\n",
       "  'shallow',\n",
       "  'beautiful',\n",
       "  'definitly',\n",
       "  'require'],\n",
       " ['wash',\n",
       "  'easily',\n",
       "  'buy',\n",
       "  'come',\n",
       "  'try',\n",
       "  'wash',\n",
       "  'clean',\n",
       "  'start',\n",
       "  'get',\n",
       "  'take',\n",
       "  'clean',\n",
       "  'far',\n",
       "  'bad',\n",
       "  'clean',\n",
       "  'come',\n",
       "  'easily',\n",
       "  'regular'],\n",
       " ['barely',\n",
       "  'red',\n",
       "  'enough',\n",
       "  'obviously',\n",
       "  'find',\n",
       "  'more',\n",
       "  'pink',\n",
       "  'dark',\n",
       "  'red',\n",
       "  'sure',\n",
       "  'still',\n",
       "  'legal',\n",
       "  'expect',\n",
       "  'otherwise',\n",
       "  'work',\n",
       "  'fine'],\n",
       " ['good',\n",
       "  'last',\n",
       "  'good',\n",
       "  'real',\n",
       "  'remain',\n",
       "  'loose',\n",
       "  'namely',\n",
       "  'usual',\n",
       "  'take',\n",
       "  'again',\n",
       "  'get',\n",
       "  'pretty',\n",
       "  'deep',\n",
       "  'finally',\n",
       "  'come',\n",
       "  'good',\n",
       "  'certainly',\n",
       "  'worth',\n",
       "  'come',\n",
       "  'just',\n",
       "  'long'],\n",
       " ['several',\n",
       "  'nautical',\n",
       "  'so',\n",
       "  'disappointing',\n",
       "  'later',\n",
       "  'so',\n",
       "  'much',\n",
       "  'bad',\n",
       "  'original',\n",
       "  'great',\n",
       "  'play',\n",
       "  'completely',\n",
       "  'miscast',\n",
       "  'whale',\n",
       "  'bad',\n",
       "  'original',\n",
       "  'almost',\n",
       "  'comical',\n",
       "  'glad',\n",
       "  'put'],\n",
       " ['terrible',\n",
       "  'even',\n",
       "  'finish',\n",
       "  'read',\n",
       "  'think',\n",
       "  'find',\n",
       "  'very',\n",
       "  'boring',\n",
       "  'disgusting',\n",
       "  'definitely',\n",
       "  'bad',\n",
       "  'ever',\n",
       "  'read'],\n",
       " ['still',\n",
       "  'excellent',\n",
       "  'even',\n",
       "  'remain',\n",
       "  'relevant',\n",
       "  'consistently',\n",
       "  'perhaps',\n",
       "  'consciously',\n",
       "  'most',\n",
       "  'assuredly',\n",
       "  'maybe',\n",
       "  'even',\n",
       "  'inevitably',\n",
       "  'undermine',\n",
       "  'daily'],\n",
       " ['only',\n",
       "  'wear',\n",
       "  'very',\n",
       "  'well',\n",
       "  'make',\n",
       "  'wear',\n",
       "  'hold',\n",
       "  'well',\n",
       "  'even',\n",
       "  'get',\n",
       "  'last',\n",
       "  'also',\n",
       "  'say',\n",
       "  'very',\n",
       "  'comfortable'],\n",
       " ['streisand',\n",
       "  'live',\n",
       "  'amazing',\n",
       "  'subsequent',\n",
       "  'european',\n",
       "  'quite',\n",
       "  'powerful',\n",
       "  'alone',\n",
       "  'numb',\n",
       "  'rich',\n",
       "  'deep',\n",
       "  'so',\n",
       "  'move',\n",
       "  'fantastic',\n",
       "  'brilliant',\n",
       "  'especially',\n",
       "  'able',\n",
       "  'see',\n",
       "  'want',\n",
       "  'more',\n",
       "  'go',\n",
       "  'along'],\n",
       " ['run',\n",
       "  'lose',\n",
       "  'look',\n",
       "  'now',\n",
       "  'believe',\n",
       "  'buy',\n",
       "  'operate',\n",
       "  'never',\n",
       "  'run',\n",
       "  'impressed'],\n",
       " ['high',\n",
       "  'wonderful',\n",
       "  'exciting',\n",
       "  'good',\n",
       "  'so',\n",
       "  'far',\n",
       "  'say',\n",
       "  'good',\n",
       "  'great',\n",
       "  'endear',\n",
       "  'great',\n",
       "  'perfect',\n",
       "  'good',\n",
       "  'tell',\n",
       "  'use',\n",
       "  'artistic',\n",
       "  'great',\n",
       "  'great',\n",
       "  'too'],\n",
       " ['understand', 'main', 'read', 'never', 'get', 'first', 'now', 'understand'],\n",
       " ['other', 'report', 'slow', 'sum'],\n",
       " ['break', 'surprising', 'popular', 'enjoyable', 'last'],\n",
       " ['great',\n",
       "  'recently',\n",
       "  'just',\n",
       "  'comfortable',\n",
       "  'supportive',\n",
       "  'right',\n",
       "  'previous',\n",
       "  'first',\n",
       "  'just',\n",
       "  'wear',\n",
       "  'constant',\n",
       "  'unfortunately',\n",
       "  'second',\n",
       "  'believe',\n",
       "  'so',\n",
       "  'sole',\n",
       "  'however',\n",
       "  'remain',\n",
       "  'fair',\n",
       "  'terrific',\n",
       "  'get',\n",
       "  'second',\n",
       "  'black'],\n",
       " ['favorite',\n",
       "  'absolutely',\n",
       "  'grow',\n",
       "  'first',\n",
       "  'hear',\n",
       "  'kinda',\n",
       "  'crazy',\n",
       "  'think',\n",
       "  'pleasantly',\n",
       "  'surprised',\n",
       "  'enough',\n",
       "  'write',\n",
       "  'own',\n",
       "  'put',\n",
       "  'good',\n",
       "  'appreciate',\n",
       "  'good',\n",
       "  'enjoy',\n",
       "  'easy',\n",
       "  'funky',\n",
       "  'come',\n",
       "  'soon'],\n",
       " ['horrible',\n",
       "  'terrible',\n",
       "  'allow',\n",
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       " ['say', 'already', 'say'],\n",
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       " ['say',\n",
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       " ['think',\n",
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       "  'firm',\n",
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       " ['knock', 'upset', 'see', 'legal'],\n",
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       " ['poor', 'sad', 'ever', 'read', 'poor', 'find', 'happy'],\n",
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       " ['get', 'say', 'ever', 'worry', 'keep', 'dead', 'just'],\n",
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       " ['good',\n",
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       " ['thoroughly',\n",
       "  'debunk',\n",
       "  'typical',\n",
       "  'full',\n",
       "  'skewed',\n",
       "  'many',\n",
       "  'weird',\n",
       "  'present',\n",
       "  'factual',\n",
       "  'exist',\n",
       "  'several',\n",
       "  'most',\n",
       "  'notably',\n",
       "  'thoroughly',\n",
       "  'debunk',\n",
       "  'true',\n",
       "  'never',\n",
       "  'stop',\n",
       "  'take',\n",
       "  'good',\n",
       "  'rankle',\n",
       "  'serious'],\n",
       " ['very',\n",
       "  'functional',\n",
       "  'very',\n",
       "  'pretty',\n",
       "  'many',\n",
       "  'get',\n",
       "  'stay',\n",
       "  'securely',\n",
       "  'try',\n",
       "  'now',\n",
       "  'fit',\n",
       "  'securely',\n",
       "  'even',\n",
       "  'seem',\n",
       "  'clear',\n",
       "  'now',\n",
       "  'fit',\n",
       "  'perfectly',\n",
       "  'clear',\n",
       "  'yellowish',\n",
       "  'make',\n",
       "  'look',\n",
       "  'dirty+much',\n",
       "  'large',\n",
       "  'think',\n",
       "  'quite',\n",
       "  'conspicuous'],\n",
       " ['enjoy',\n",
       "  'diverse',\n",
       "  'first',\n",
       "  'foremost',\n",
       "  'then',\n",
       "  'say',\n",
       "  'follow',\n",
       "  'closely',\n",
       "  'enjoy',\n",
       "  'cleverly',\n",
       "  'add',\n",
       "  'latter',\n",
       "  'certainly',\n",
       "  'add'],\n",
       " ...]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = []\n",
    "for idx in train_x.values:\n",
    "    corpus.append(nltk.word_tokenize(idx))\n",
    "\n",
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4ab2476d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec<vocab=1802, vector_size=300, alpha=0.025>\n"
     ]
    }
   ],
   "source": [
    "# parameters:\n",
    "# min_count: ignore all terms with total freq less than the num\n",
    "# negative: how many noise words would be drawn (hs=0)\n",
    "# hs: {0,1}, if 1 = softmax; 0 for negative sampling\n",
    "# sg: training algorithm, 1 for skip-gram, others for CBOW\n",
    "# window: the max distance between the current word and the predicted word within a sentence\n",
    "\n",
    "word_model = Word2Vec(corpus, \n",
    "                      min_count = 5,\n",
    "                      vector_size = 300, # features\n",
    "                      window = 5, \n",
    "                      sg = 1, # skip-gram\n",
    "                      hs = 0,\n",
    "                      negative = 5,\n",
    "                      sample = 0.001,\n",
    "                      workers = workers,\n",
    "                      compute_loss = True,\n",
    "                      alpha = 0.025\n",
    "                     )\n",
    "\n",
    "print(word_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "192b205f",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_model.save('word2vec.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "23c78830",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import word2vec\n",
    "import numpy as np\n",
    "model = word2vec.Word2Vec.load('word2vec.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "187e4465",
   "metadata": {},
   "outputs": [],
   "source": [
    "WordVect = {}\n",
    "for idx, key in enumerate(list(model.wv.index_to_key)):\n",
    "#     print(key)\n",
    "    WordVect[key] = model.wv[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "eb225ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>0.125563</td>\n",
       "      <td>0.239395</td>\n",
       "      <td>0.027019</td>\n",
       "      <td>0.044875</td>\n",
       "      <td>0.069484</td>\n",
       "      <td>-0.079739</td>\n",
       "      <td>0.082444</td>\n",
       "      <td>0.330290</td>\n",
       "      <td>0.031900</td>\n",
       "      <td>-0.124213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010313</td>\n",
       "      <td>0.193715</td>\n",
       "      <td>-0.034518</td>\n",
       "      <td>0.104229</td>\n",
       "      <td>0.201077</td>\n",
       "      <td>0.154480</td>\n",
       "      <td>0.019106</td>\n",
       "      <td>-0.025293</td>\n",
       "      <td>0.047754</td>\n",
       "      <td>-0.140803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>0.073884</td>\n",
       "      <td>0.130609</td>\n",
       "      <td>-0.030139</td>\n",
       "      <td>-0.001287</td>\n",
       "      <td>0.020714</td>\n",
       "      <td>-0.088849</td>\n",
       "      <td>0.022139</td>\n",
       "      <td>0.321064</td>\n",
       "      <td>-0.006537</td>\n",
       "      <td>-0.059662</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032288</td>\n",
       "      <td>0.181782</td>\n",
       "      <td>-0.000827</td>\n",
       "      <td>0.103629</td>\n",
       "      <td>0.132230</td>\n",
       "      <td>0.242736</td>\n",
       "      <td>-0.040601</td>\n",
       "      <td>-0.061482</td>\n",
       "      <td>0.030390</td>\n",
       "      <td>-0.102584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very</th>\n",
       "      <td>0.089850</td>\n",
       "      <td>0.262118</td>\n",
       "      <td>0.096784</td>\n",
       "      <td>0.018785</td>\n",
       "      <td>0.092545</td>\n",
       "      <td>-0.022089</td>\n",
       "      <td>0.043503</td>\n",
       "      <td>0.245814</td>\n",
       "      <td>0.012561</td>\n",
       "      <td>-0.011269</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024308</td>\n",
       "      <td>0.105959</td>\n",
       "      <td>0.040948</td>\n",
       "      <td>0.062490</td>\n",
       "      <td>0.233085</td>\n",
       "      <td>0.129438</td>\n",
       "      <td>0.037585</td>\n",
       "      <td>-0.059100</td>\n",
       "      <td>0.116573</td>\n",
       "      <td>-0.084572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>0.012621</td>\n",
       "      <td>0.131089</td>\n",
       "      <td>0.024189</td>\n",
       "      <td>0.111639</td>\n",
       "      <td>0.048162</td>\n",
       "      <td>-0.018788</td>\n",
       "      <td>0.098401</td>\n",
       "      <td>0.229515</td>\n",
       "      <td>-0.048814</td>\n",
       "      <td>-0.101965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>0.199622</td>\n",
       "      <td>-0.039677</td>\n",
       "      <td>0.098955</td>\n",
       "      <td>0.194240</td>\n",
       "      <td>0.156429</td>\n",
       "      <td>0.045350</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>0.110466</td>\n",
       "      <td>-0.118983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>so</th>\n",
       "      <td>0.124005</td>\n",
       "      <td>0.192243</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>-0.064151</td>\n",
       "      <td>-0.001239</td>\n",
       "      <td>-0.080482</td>\n",
       "      <td>0.018324</td>\n",
       "      <td>0.314623</td>\n",
       "      <td>0.076509</td>\n",
       "      <td>-0.079386</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057440</td>\n",
       "      <td>0.173106</td>\n",
       "      <td>0.079787</td>\n",
       "      <td>0.021859</td>\n",
       "      <td>0.149153</td>\n",
       "      <td>0.153801</td>\n",
       "      <td>-0.011348</td>\n",
       "      <td>-0.068694</td>\n",
       "      <td>0.052541</td>\n",
       "      <td>-0.113169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6    \\\n",
       "good   0.125563  0.239395  0.027019  0.044875  0.069484 -0.079739  0.082444   \n",
       "get    0.073884  0.130609 -0.030139 -0.001287  0.020714 -0.088849  0.022139   \n",
       "very   0.089850  0.262118  0.096784  0.018785  0.092545 -0.022089  0.043503   \n",
       "great  0.012621  0.131089  0.024189  0.111639  0.048162 -0.018788  0.098401   \n",
       "so     0.124005  0.192243  0.003185 -0.064151 -0.001239 -0.080482  0.018324   \n",
       "\n",
       "            7         8         9    ...       290       291       292  \\\n",
       "good   0.330290  0.031900 -0.124213  ...  0.010313  0.193715 -0.034518   \n",
       "get    0.321064 -0.006537 -0.059662  ...  0.032288  0.181782 -0.000827   \n",
       "very   0.245814  0.012561 -0.011269  ... -0.024308  0.105959  0.040948   \n",
       "great  0.229515 -0.048814 -0.101965  ...  0.008132  0.199622 -0.039677   \n",
       "so     0.314623  0.076509 -0.079386  ...  0.057440  0.173106  0.079787   \n",
       "\n",
       "            293       294       295       296       297       298       299  \n",
       "good   0.104229  0.201077  0.154480  0.019106 -0.025293  0.047754 -0.140803  \n",
       "get    0.103629  0.132230  0.242736 -0.040601 -0.061482  0.030390 -0.102584  \n",
       "very   0.062490  0.233085  0.129438  0.037585 -0.059100  0.116573 -0.084572  \n",
       "great  0.098955  0.194240  0.156429  0.045350  0.016602  0.110466 -0.118983  \n",
       "so     0.021859  0.149153  0.153801 -0.011348 -0.068694  0.052541 -0.113169  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WordVect = pd.DataFrame(WordVect)\n",
    "WordVect = WordVect.transpose()\n",
    "WordVect.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b40675e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv['expensive'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "105b6f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wear', 0.9453682899475098),\n",
       " ('work', 0.9321094155311584),\n",
       " ('break', 0.9311400651931763),\n",
       " ('arrive', 0.9130638241767883),\n",
       " ('run', 0.9108525514602661)]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('cheap', topn=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "61c81720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6232495\n",
      "0.7949096\n"
     ]
    }
   ],
   "source": [
    "# Checking top 10 similarity score between two word in \"good\"\n",
    "print(model.wv.similarity('good','cheap'))\n",
    "print(model.wv.similarity('good','expensive'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7e7e47a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('right', 0.0014160819)]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the prob distribution of center words given context words.\n",
    "model.predict_output_word(['expensive','books'], topn=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "705923c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('small', 0.0026049132)]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the prob distribution of center words given context words.\n",
    "model.predict_output_word(['cheap','books'], topn=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ba26202e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tsne plot for below word\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "4afa54ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def tsne_plot(for_word, w2v_model, item_range=10):\n",
    "    fig = plt.figure(num=None, figsize=(8,8), dpi=100)\n",
    "    \n",
    "    dim_size = w2v_model.wv.vectors.shape[1]\n",
    "    \n",
    "    arrays = np.empty((0, dim_size), dtype='f') # creating an empty array\n",
    "    word_labels = [for_word]\n",
    "    color_list = ['red']\n",
    "    \n",
    "    # add the words' array to arrays\n",
    "    arrays = np.append(arrays, w2v_model.wv.__getitem__(word_labels), axis=0) # shape=(300, )\n",
    "    \n",
    "    # get a list of the most similar words related to for_word\n",
    "    similar_words = w2v_model.wv.most_similar(for_word, topn = item_range)\n",
    "    \n",
    "    for word_score in similar_words:\n",
    "        # word_score: 0 is for words, 1 is for scores\n",
    "        word_vect = w2v_model.wv.__getitem__([word_score[0]])\n",
    "        word_labels.append(word_score[0])\n",
    "        color_list.append('green')\n",
    "        arrays = np.append(arrays, word_vect, axis=0)\n",
    "        \n",
    "    # project the 10 arrays of shape(1, 300) into 10 dimensions each\n",
    "    model_PCA = PCA(n_components=10).fit_transform(arrays)\n",
    "    \n",
    "    # find 2D coordinates t-SNE\n",
    "    np.set_printoptions(suppress=True)\n",
    "    Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(model_PCA)\n",
    "    \n",
    "    # Plot\n",
    "    df_plot = pd.DataFrame({'x':Y[:, 0],\n",
    "                            'y':Y[:, 1],\n",
    "                            'name': word_labels,\n",
    "                            'color':color_list})\n",
    "    \n",
    "    plot_dot = sns.regplot(data = df_plot, \n",
    "                           x='x',\n",
    "                           y='y',\n",
    "                           fit_reg = False,\n",
    "                           marker='o',\n",
    "                           scatter_kws={'s': 40,\n",
    "                                        'facecolors': df_plot['color']})\n",
    "    \n",
    "    for line in range(0, df_plot.shape[0]):\n",
    "         plot_dot.text(df_plot[\"x\"][line],\n",
    "                       df_plot['y'][line],\n",
    "                       '  ' + df_plot[\"name\"][line].title(),\n",
    "                       horizontalalignment='left',\n",
    "                       verticalalignment='bottom', size='medium',\n",
    "                       color=df_plot['color'][line],\n",
    "                       weight='normal').set_size(12)\n",
    "\n",
    "\n",
    "    plt.xlim(Y[:, 0].min()-100, Y[:, 0].max()+100)\n",
    "    plt.ylim(Y[:, 1].min()-100, Y[:, 1].max()+100)\n",
    "\n",
    "    plt.title('t-SNE visualization for word \"{}'.format(for_word.title()) +'\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "0cc35236",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne_plot(for_word = 'cheap', w2v_model= model, item_range=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "ba6d6664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hYWGMGDGCXr16cejQoav2+I0aNYq77rqLESNGULZs2QKNOSwsjPr167NkyRIcHBxISEigR48eVy0fGBjIyJEjefvtt9m3bx9Lly7lm2++wcvLi3feecfac2cymayXnf99Y9mCBQv44IMPePHFF+nXr581kXr22WfZuXPndce+Y8cO3nzzTe677z7uu+++XOs2b97MK6+8wsCBA3n00Uetd9yPHTs21w1x/8XLyyvfhO7cuXPApTYuqITk8heKq+3Px8fnhuts3bo169atIyAggPDwcAIDA2nSpAnTpk1j7dq1ODg4WMfZenl55TsW/MpjvRofH598t72czN1uGRkZjB49mqpVq3L48GG++eYbnnzyyWvGYjKZSEhIwNfXFzc3NwwGAw899BDdunXLU/+/v+z8l1atWtGqVSvS09PZsGED06ZNY8yYMdSvX5969erlKd+lSxfee+89li1bxvHjxwkODqZx48YAeHp6AjB8+HDrsit5eXndUGwixZFuqhMpwf49HVq1atWoU6eO9V+5cuWws7MjICCA2bNn5zuDwtGjR4FrX/4ODg7mlVdeISoqihUrVhTsQXDpkvjq1atZuHAh9evXJzw8PN9yW7dupXnz5uzYsQODwUCNGjV4/vnnqVq1KrGxscD/hmhcOSvCli1bctUTHR2Nh4cHTzzxhDUZTk1NJTo6+po3AV7p7NmzPP3009SrVy/fMaZbt27FbDYzbNgwazJsMpmsN5ld3s9/TWkXGRnJ33//navn2mQysWjRIurUqZPnsvytqFChAv7+/ixYsCDX8pMnT7Jt2zYaNmx4w3W2bduWnTt3snLlSuvUbk2bNuXs2bNMnz6dZs2aWZO9yMhITp8+necLw/z583FwcLjmTVpNmzbl1KlTub7QxMfHs23bthuO+WZ8/PHHnDlzhk8//ZQHH3yQiRMnsn///lxl1qxZk+sL64oVK8jJyaFZs2a4u7tTs2ZNjhw5kuszXKVKFSZOnHjNWWT+7cMPP6Rfv35YLBZcXFxo166ddWz5lZ+LK3l4eNCuXTtWrFjB0qVL6d69u/W9WbFiRcqUKcOpU6dyxRYUFMTHH3+c58qBSEmkHmKREszT05OtW7eyfv16atasedWemjfeeINBgwbRp08fHnjgAWrUqIHZbGbTpk18//333HvvvXlmCfi3e+65h6VLl7J27Vprj9GV9u7de9VL02XLls1zufhK3bp14/3332fRokX5JpeX1axZE2dnZ4YPH87QoUPx8/Nj3bp17N271zq+uU2bNrz//vvWGQNiY2OZOHEibm5u1nrq1q3LTz/9xAcffEC7du2Ii4tjypQpnD9//rp6u7Kysnj66afJyclhyJAh7N27N1ciHRQUZE3e3n33Xfr27UtSUhIzZsxg3759wKWb59zd3fH09OT8+fOsWrWKGjVq5NnXkCFDWL16NQ888ABPPPEEjo6OzJgxg5MnT/Ltt9/+Z6w3wmg08sILL/Daa6/x/PPP06tXLxISEpg4cSJeXl48/PDDN1xns2bNsLOz4++//7bO4FGzZk08PT3ZsmUL7777rrVsnz59mDlzJkOGDGHYsGGUL1+ev/76i9mzZzNkyJB833eX9ezZk2nTpjFkyBCef/553N3dmTx58nV/wbmaEydOXDWp9vT0pGLFimzatInp06fz3HPPUbFiRYYOHcoff/zBq6++yi+//IKDgwMAsbGxPPXUUzzwwAPExMQwfvx4WrZsaf2i8MILL/DEE0/w4osv0qNHD0wmE9999x3bt2/nqaeeuu6YmzVrxtSpU3n11Vfp0aMH2dnZfPvtt3h7e9O0adOrbterVy+eeeYZTCZTrqs0dnZ2PP/887z11lvY2dnRrl07kpKSmDRpEmfPnr3mDDciJYUSYpESbMCAAezatYvHH3+c999/n+7du+dbrnbt2sydO5evvvqKGTNmcO7cOezs7KhcuTKvv/46/fr1u679XR46kZ8hQ4Zcdbvhw4fz6KOPXnW9t7c3bdq0YdWqVXTt2vWq5ZycnPjuu+/4+OOPGT16NElJSYSHh/Puu+/Sp08f4FIv54cffsjkyZN54oknqFSpEu+99x7vvfeetZ7evXtz6tQpZs+ezcyZMwkMDKRNmzbcf//9vPnmmxw6dOiaXxDi4uLYtWsXQL43Gg4ZMoShQ4fy1ltvMXXqVJYuXYqfnx9NmjRh4sSJPPPMM0RHR9OmTRv69OnDqlWreOaZZxg2bFie469SpQozZ85k/PjxvP766xgMBurWrcu0adOss1cUpD59+uDm5sZXX33FM888g7u7O61ateKFF1646pMIr8XZ2ZkmTZqwevVq6+V2o9FIo0aN+Ouvv3KNS3dxcWH69Ol8/PHHfPbZZ6SkpFCxYkVGjx79n+9RR0dHfvjhB8aMGcPo0aMxGAzcc889lC9fPtd0cTdq8uTJTJ48Od91bdu2ZcKECbz22mtUrVrV+h53dXXl7bff5oknnmDy5MnWoUrdunXD09OT5557DldXV3r37s3zzz9vra9ly5ZMmTKFiRMnMmzYMBwcHKhVqxZTp069oZsDW7duzbhx4/juu+8YMmQIBoOBiIgIpk2bds1x9q1atcLLy4ugoKA88zHffffduLm58e233zJr1ixcXV1p2LAh48aNo3z58tcdm0hxZbDcyB0XIiIicsPat29P48aNc93kKiLFh8YQi4iIiIhNU0IsIiIiIjZNQyZERERExKaph1hEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumB3PcJIvFgtms+xELitFo0PksxdS+pZ/auHRT+5ZupbV9jUYDBoPhusoqIb5JZrOF+PjUog6jVLC3N+Lj40ZSUho5Obf2mFUpftS+pZ/auHRT+5Zupbl9fX3dsLO7voRYQyZERERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsWolIiC9evMhbb71F69atadiwIffddx+bN2+2rt+7dy8DBw6kfv36tG3blilTpuTa3mw289lnn9GqVSvq1avHI488wvHjxwv7MERERESkGCoRCfELL7zA9u3bGT9+PL/99hu1atXi0Ucf5fDhwyQkJPDwww8THh7O7NmzGTp0KJ9++imzZ8+2bj9p0iR+/vlnRo0axaxZszAYDDz++ONkZWUV4VGJiIiISHFgX9QB/Jfjx4+zdu1afvrpJxo2bAjAiBEjWL16NQsXLsTZ2RlHR0dGjhyJvb09lSpV4vjx43zzzTf07duXrKwsvvvuO15++WXatGkDwIQJE2jVqhXLli2jW7duRXl4IiIiIlLEin0PsY+PD19//TW1a9e2LjMYDFgsFhITE9m8eTORkZHY2/8vt2/atClHjx7lwoUL7Nu3j9TUVJo2bWpd7+npSc2aNdm0aVOhHouIiIiIFD/FvofY09PT2rN72ZIlSzhx4gQtW7ZkwoQJVK1aNdf6gIAAAM6cOUNsbCwAwcHBecrExMTcUmz29sX++0SJYGdnzPW/lC5q39JPbVy6qX1LN7XvJcU+If636OhoXn/9dTp06ED79u15//33cXR0zFXGyckJgMzMTNLT0wHyLZOYmHjTcRiNBnx83G56e8nL09OlqEOQ20jtW/qpjUs3tW/pZuvtW6IS4uXLl/PSSy9Rr149xo8fD4Czs3Oem+MyMzMBcHV1xdnZGYCsrCzrz5fLuLjcfOObzRaSktJuenv5Hzs7I56eLiQlpWMymYs6HClgat/ST21cuql9S7fS3L6eni7X3fNdYhLiGTNmMHr0aDp16sS4ceOsPb5BQUHExcXlKnv5dWBgIDk5OdZloaGhucpUr179lmLKySldb5yiZjKZdU5LMbVv6ac2Lt3UvqWbrbdviRgwMnPmTN577z0GDBjAJ598kmv4Q2RkJNHR0ZhMJuuy9evXU6FCBcqUKUP16tVxd3dn48aN1vVJSUns2bOHRo0aFepxiIiIiEjxU+wT4qNHjzJmzBg6derE4MGDuXDhAufOnePcuXMkJyfTt29fUlJSGDFiBIcOHWLOnDn88MMPDB48GLg0dnjgwIGMGzeOFStWsG/fPp5//nmCgoLo1KlTER+diIiIiBS1Yj9k4o8//iA7O5tly5axbNmyXOt69+7NBx98wLfffsvo0aPp3bs3/v7+DB8+nN69e1vLDRs2jJycHN544w0yMjKIjIxkypQpeW60ExERERHbY7BYLJaiDqIkMpnMxMenFnUYpYK9vREfHzcSElJtevxSaaX2Lf3UxqWb2rd0K83t6+vrVvpuqhOR0mts1BjGbf4g1zI7gx1eTl40CW7Oa03epLpvjSKKTkRESjslxCJSbCzus9z6s8li5lTyCd7f+B49fu/Mmvs2EegaWITRiYhIaaWEWESKjUZBjXO9bhLclBD3cvSY24VZ+35kWMMXiigyEREpzYr9LBMiYtvqBTQA4GTySeuyxUcW0v33zlT4pizlvvSj+cwIpuz8CoAccw51vq/Kk8seyVNXi5mNGPbXU4UTuIiIlBhKiEWkWDt08SAA4Z4VAFh2bCkPLb2fev71mXbnT3zXZTrlPUJ5bc3LRMVsxN5ozz3V7mPJ0UWkZCVb64k+u4mDFw9wX/WBRXIcIiJSfCkhFpFiI8ecY/2XnJXEhpj1PP/3EDwdvehXrT8A+xP2c0+1+xjV8kNalWvDHeF38mWnKQCsP/MPAPfXGEh6TjoLDs+z1j1r30zCPSvQNLh54R+YiIgUaxpDLCLFRtkvffMsq+ZTnaldZlhvqBvS4FkAUrNTOZp4hCMXD7E1bgsAWeYsACp5V6FJcDN+PfAz99UYSKYpk7mHZjO43jMYDIZCOhoRESkplBCLSLHxZ7+V1p8djI4EuAbi7+qfq8yF9Au8tOpZlhxdiAEDFb0q0SS4GQBXTqs+oMYDPPvX05xKPsmWs5tJzEykf7X7C+U4RESkZFFCLCLFRv2Ahv9Z5sllj3Aw4QC/9ZhPZFATnOycSMtOY8beH3KV616pF6+vGc6Cw/PYFLuRVuXaUs6j/O0KXURESjCNIRaREiUqdgPdK/WkZUhrnOycAFhx4tJj3c387ylLbg5u9Krch98P/spfJ5Zzb3X1DouISP7UQywiBerE2WSi9p3lcOx5jAYDtUKD6Ng0HFf7ghm72yAggtkHf6Guf33KuoewKXYjn0R/jAEDadlpucreX2MQXed0xMPRk24VexTI/kVEpPRRQiwiBcJisfDXltMs3LSHePMJkhwOYMDI3i01Wb37CPe0rk3DKv7/XdF/+LzDl7y2+iVeW/MyAJW8KzGu7Sf8dmAWG2PW5SrbKKgxZZzL0LViD1zsXW553yIiUjoZLFfehSLXzWQyEx+fWtRhlAr29kZ8fNxISEglJ8f83xtIsbTj8AW+/TOK406LSXPfAf/fIWzAgGdSM8JNHRjaowlhQR6FFtOWs5vpMrs9y/qtsj7gQwqePsOlm9q3dCvN7evr64ad3fWNDlYPsYjcMovFwqodJzhr2E6ax47cKw0WUn02EBcbzvo9oYWSEK89vYZ/Tq/ml/0/0SqkjZJhERG5Jt1UJyK3LDk9m2Nx50l12Zd/AQMkOe9ix9EYCuOiVHzGBSZvm4i/iz+ftp902/cnIiIlm3qIReSWmUwWzBYLFmPmVcuYjVnkZJuxYB1Ncdt0r9SL7pV63ea9iIhIaaEeYhG5ZR6uDni6uOCYWe6qZZwyy1HW1xOjnhQnIiLFjBJiEbll9nZGmtcIwy8zArtsz7zrMwLwN9WlWY2rJ8wiIiJFRUMmRKRAtKpblt3HK2OO6895l41kOh8DjLikVyIwqzkR4ZWIqHbr066JiIgUNCXEIlIgXJ3tGdy9Hn9E+RB1IISUi2kYAG9XD+5oVZUWtQI1XEJERIolJcQiUmDcnB3o07oSXRqHEhufjtEI5QM9CArwLJVzXIqISOmghFhECpyrswMVyzoAlyZ9FxERKc70l0pEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREodi8VS1CGISAliX9QBiIhIyfXzvh8Z9tdT1yyzqv8GapSpWUgRwf74fTz/9xAW911uXRYwyZOXGr3K8MavF1ocIlJyKCEWEZFbNueeObhZvDGZzHnWhXtVKNRY5h2aw+azUYW6z8JyIuk4n2/9hL9PruBsagzeTj7U86/Po3UG0y60ww3XNzZqDOM2f0Dc00kA9JrbFYC5vRYXaNwixZ0SYhERuWUNghvgZfEnJydvQiwFY/WplTy0ZADBbsE8U38Y1XyqcyHjPLMP/Er/hb0ZXPdp3mv5wS3t48PW4wsoWpGSRWOIRUTktkvJSiZiem2az4wg05QJXBrne/f8ntScWpG4tDjgUg/oM8ufoM73VSn7pS81p1bkmeVPEJ9xwVqXxWJhys6vafVTY0K/CqDxjHp8vvUTLBaLtccTLg2TGBs1JlccOeYc6nxflSeXPZInxhYzG/3n8I+iEpNyhkf/eIBGQZGsuOcfHq79GM1DWtK9Ui++v/NH3m0xhq92TOLHPdNuaT/VfKtTzbd6AUUtUnKoh1hERG6ZyWwix5JDjjl3D7HRYMRoMOLu6MEn7b6g3/wefBI9jlcaj+C7Xd+w6tTfTO86iwDXANKy0+g9rxtlnMvwQeuP8XLyYmPMesZt+gBne2c+bvsZAKM3vMMX2z7lyXpDaFO+HTvObWfMhnfIMmUysOaDxKSe4ce901jcZzll3UNyxWNvtOeeavfx7c4vSclKxt3RA4Dos5s4ePEA49p+Wjgn7AZ9tWMSKVnJTGg7EWd75zzrn6w3hHmHfmd89FjurzEIg8FAr7ldqeBVkQpelZi66xvOp5+jjl893mv5PhGBkfnu599DJgImefJR2/HsS9zN7D1zyDJl0T60Ix+0/pgA1wDrdkuOLmL85rHsi9+Dp6MXvSr34fWmb+Pm4HYbzoZIwVNCLCIit6zy55XzXd6qXFtm95j//z+34eHaj/H5lglEBjXmvfVv80DNR+gcficAhxMPUdY9hM/aT6aCV0UAWoa0ZlvcFtad+QeAxMyLTN7+OY/VfZK3m78HQNvy7Tmffo4NZ9bxYqNXCHYrC0CjoMb5xnR/jYF8vnUCCw7P474aAwGYtW8m4Z4VaBrcvIDOSMH6+8Ry6vjVJcSj3FXL9KrchzfXvsau8zuo418PgAWH51HFpyqjW47FgoWR60bw6NIHiB60Czuj3XXt+71179CnZm++7fI9RxKO8Nba13jzH3u+umMqALMP/MJTyx+jb5V7eK3JG5xIOsGYje+wL2Efv3Wfh8FguPUTIHKbKSEWEZFbNv/e+fneVOfx/z2wl73Z7F3+OrGc+xfdTQWvirzb4n9DGur41WVB7z8wW8wcSzzKkcTD7Ivfy4GE/ZjMJuBST262OZtuFbrnqvfKev5LJe8qNAluxq8Hfua+GgPJNGUy99BsBtd7ptgmb8eTjtMx7I5rlrn8JeJE8glrQpxjzuaX7r/j4egJXBq6MvSvJ9l1fgf1Ahpc175r+tVkas+pJCSk0jqkHVvjopl/eC5wafjKe+vfpn1oRyZ3+ta6TUXvSvSb34Plx/+gU3iXGz1ckUKnhFhERG5ZncA613VTnZuDG90r9eLzrRNoGdIGVwfXXOsnb5vIZ1s+5kLGBfxdAqjnXx9XezeSshIBiM+IB8DPxf+W4h1Q4wGe/etpTiWfZMvZzSRmJtK/2v23VOftZcHeeO0/2ZfXXzkHczXf6tZkGLAOIUnLSbvuPUcGNcn1Oti9LGnZqQAcuniQM6mneTbiRXLMOdYyzcu2xMPRk1Wn/lZCLCWCbqoTEZFCsz9+H1/vmERtv7pM3zOVDTHrretmH/iFt9e9zlP1h7Hn4SPsfvgQM+/6jUre/xuO4eXoBcD5jPO56j2dfIp/Tq8m25R9XXF0r9QLNwd3Fhyex9xDc2hVri3lPMoXwBHeHuU9QjmZfOKaZY4lHfv/sv87Dhf73F84jIZLf/bNluufDcTF3iV3HRixcCnpvvwF5ZXVL1D2S99c/5KzkohNjb3u/YgUJfUQi4iIlcViIT4pk2yTGW93R5wdC+7PRI45h6ErBlPeI5SFvf+kz7xuDFvxJH/3X4ebgxsbY9bj6ejFsIbPW7dJyU5hY8x6a+9nw8BIHIwOLDmykKbBzazlvtoxiV/2z2TXQ4eua2ysm4MbvSr34feDv3Ig4QDj2n5SYMd5O3QO78qk7Z9xOvnUVccRLzw8jxD3ctbhEoXBy+nSF5S3m42iRUjLfNZ7F1osIrdCCbGIiGCxWNh68Dwrtx/n5IV4zBYzro7ONKkaSvuG5fB0c7zm9ltjtl71wRzlPUIJdAvi0y0fs/3cNub1Xoqrgyvj2n7GHb+14b31b/FB649pGNiI73dP4a21r9M5/E5iU2P4YttnxKWdxfv/E6syLmV4ou7TfLXjC5ztnWjx/zfdTdn5FSOajsTeaG/tRZ5z8FciAiMJ8wzPN+b7awyi65yOeDh60q1ij1s6f7fb43WfZMbe7xn299P82PWXPDNNTN31Lf+cXs3Y1hOsvcCFoYp3Vfxc/DmRfIxnAoZZl59NO8vTyx/noVqPWMc2ixRnSohFRIRlm0+xYNNOYo3RpLntw2LIxCGzLGd3RLL3ZBWe7tEAL3enq27f55c+V133drNRtCnfjvGbx/JgrUesPbu1/erwZL0hfLH1U7pV7EH/avdzPOkYP+2dwfe7viXILZhOYZ15uPZjvLhyGPvj91HNtzpvNXsXf9cAvt/1LZO2fU6oRxijWn7Iw7UfA+CuSj359cDPDF3xJANqPMDYNhPyjatRUGPKOJeha8UeeYYF3C7JaVls2hfHhn0nSUrLxNPViSbVytO4RgAerlf/0hHoFsS3nafxyNKBdPq1NY/XfYoqPlVJyEhg/uE5/H5wNo/UfpyHaj9aKMdxmZ3RjtebvMVLq57FzmDHHeF3kpSZyMebxxKTepq6/vULNR6Rm6WEWETExp2MS2FJ9F5OOC8izWOXdXm20zky3A5ivnAPizf4cF/Hanm2vbf6AAbWHoSPjxsJCanXvKnu9JMX8ix7q9m7vNXsXevrVxqP4JXGI/KUG1TzIevPBoOBp+sP5en6Q/PdT5BbMH/0W5lr2eVHE19py9nNXMi4wANX1H07xV1M5+tF2zh+8QQXHLaR43AR+xRvjm6oz/q95XnirgYEeF89MW8Z0pq/7lnLV9u/YNK2zziTchpPJy8aBkTw012zaR/asVCO498G1nwQD0cPJm79lOl7vsfNwY3GQU2Z3Onbq/bOixQ3BsuVt6PKdTOZzMTHpxZ1GKWCvb3xuv6YSsmk9i3+5qw6zLzdf3PGbzrkM+uYS0oNqmXfzZsDWuU7dKKktfHa02v45/Rqftn/E+GeFZjdc8Ft36fZYuGT36LZem4Tcb5zMdtlWNcZTM4Exveivn8kz/eLwFjMpn4rae0rN6Y0t6+vrxt2dtc3hEizTIiI2LijcRdIdjiUbzIMkOl8nLTsNM4mXP9UXcVZfMYFJm+biL+LP5+2n1Qo+zxyOomj5+K44PlXrmQYwGKXwQXPvzh2Lo7DpxMLJR4RyU1DJkREbJy9nR1Yrv7nwGCxx2AwFruey5vVvVIvulfqVaj7PBKTRCpxZDvG5bs+2zGOFOI4eiaJKuW8CzU2EVEPsYiIzatRzh/f7FpgyX+6Muf0yni7eFDO372QIys9LBYLZoPpqr3wGMBiyMGsQYwiRUIJsYiIjWtUPQA/p2C8ElqDJXfG5pAZREBGC1rUDMPJ8b/n95X8BZdxxdUcgF22Z77r7bI9cTUHUtbPNd/1InJ7aciEiIiN8/V0ZmCHOkxbbubcuXAuOu3EbMzAOas8ZUw1aVSxEh0jiu9T3EqCmuG+BHmW4UJSC+J9/wDDFTcvWYx4JjUnyNOXmuG+RRekiA1TQiwiItSuUIYX+jZl455wth2tTHaOibLBXjSpHkK9ymWwv847tSV/9nZG7m9Xi2+XZuJw3pNE122Y7BOwy/HBK60+5RxrcH+72jrPIkVE067dJE27VnBK85Qvova1BWrj63cqLoW/t51k+7Ez5JizsTc6UC+8LO3ql6dcQPEco632Ld1Kc/veyLRr6iEWEREpJOUC3Bl0Rw16p1cmLTMHVyd73F0cijosEZunhFhERKSQubs4KBEWKUY0WElEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxaUqIRURERMSmKSEWEREREZumhFhEREREbJoSYhERERGxafZFHYCIyO02NmoM4zZ/kGuZs50zwe5l6RLejRcavYyXk7d13dAVT7LuzD9ED9pVyJGKiEhRUEIsIjZjcZ/lAFiwkJqdyra4LXy+9ROWHV/Kwj5/4utcBoAXGg3niaynijJUEREpREqIRcRmNApqnOt12/LtaVWuDT1+78Ko9SMZ3+5zACp4VSyK8EREpIhoDLGI2LSIwEi6VujOL/t/Ii07Dbg0ZCJieu1c5Wbs+YFWPzWm3Jd+NJhWk7FRY8gx5xRFyCIiUsCUEIuIzWsX2oEscxbb4rbku/7T6I95ceUwWpdry/Sus3ikzhNM3PoJL618tpAjFRGR20FDJkTE5gW4BgAQl3Y2z7qkzEQmRH/EA7UeYXSrscClBNrXyZfnVw7hyfpDqO5bo1DjFRGRgqUeYhGR/2cwGPIs23w2irScNLqE30mOOcf6747wOwFYdfKvwg5TREQKmHqIRW7BjU7nVRBOJB2n0Yw6fNZ+MvdWH3DVchHTa9O8bEs+7/Blge6/NIpJjQEg2C0kz7r4jHgA7lvUL99tY1Njb19gIlKsJGTEM3XXtyw6soATycfJMmVS3iOULuHdeLrBUOtMNVLylLiEeNKkSaxfv57p06dbl+3du5fRo0eza9cuvL29GTRoEI8++qh1vdlsZuLEifz6668kJSURERHB22+/TVhYWFEcgpRC1zudV0EIdAticZ/lhGsmhAKz+uRK3BzcqetfL886L0cvACZ3/JZK3pXzrPd3Cbjd4YlIMbD3wh4GLLqbLHMWj9UZTP2Ahtgb7dkcG8XXOyYx9/AcFvdZbh2CJSVLiRoy8f333/PZZ5/lWpaQkMDDDz9MeHg4s2fPZujQoXz66afMnj3bWmbSpEn8/PPPjBo1ilmzZmEwGHj88cfJysoq7EOQUqpRUGMaBTUmMqgJbcu357mIl/il++8cTzrGqPUjC3RfTnZONApqjJ+LX4HWa6t2nt/B4qMLuK/6AJztnfOsjwiKxNHoSExqDPUDGlr/ORgdeW/DSE4kHy+CqEWkMGXkZPD4nw9iNBj5+551PBfxEm3Lt6dlSGuei3iJJX3/Ij79Ah9GjS7qUOUmlYge4rNnzzJixAiio6OpUKFCrnW//PILjo6OjBw5Ent7eypVqsTx48f55ptv6Nu3L1lZWXz33Xe8/PLLtGnTBoAJEybQqlUrli1bRrdu3YrikMQGXDmd16iWH+Lq4ArAhjPreD/qPbbFbcHJzok7wu9kZPPRuRLco4lHGLVhJGtOrcQCNAqM5O3mo6juWyPfIRO7z+/i7XUj2Bwbha+zL683fStPPGaLmYlbP2HGnh84k3Kach7leazOYB6r+2ShnI+CkJltYvuh82w+cIaElHQ8XV1oVDWY+pX9cHH6719nm2OjgEs9+SlZKWyL28KX2ydSybsyrzZ+I99tfJ3LMKTBs3wYNYqUrCSah7QiJuUMH0aNxmAwUKtM7Xy3E5HSY96hORxI2M+PXX/B39U/z/owz3Ceb/Qyqdkp1mW95nYFYG6vxdZla0+vofe8bvzecxEtQloBcCr5JO+tf4u/T64g05RJo8DGjGw+ijpXXLGae3A2n2z5mCMXD+Hm4Ebb8h0Y2XwUgW5BAOw4t4131r3JtnNbMVvMNAxsxOtN3iQiMPK2nI/SqEQkxLt378bLy4v58+fzxRdfcPr0aeu6zZs3ExkZib39/w6ladOmfPXVV1y4cIHTp0+TmppK06ZNres9PT2pWbMmmzZtUkIst1W70A7MOzyHbXFbaB7SkvVn1tJvfg9alWvDN3d8T0JGAh9GjabPvG780W8lLvYunE2Npctv7Qh0C+LD1uPxcPTg480f0nded1bfuzHPPmJSztBz7p2Ee1VgcqdvSc5K4r31b3MuPS5XuZdXPc/P+2bwbMMXiQxqwvoza3lj7askZiXyYqNXCuuU3LSk1Cy+WbyDg3GniLfbRZZ9PA4p3uw6U5tKu8rxeNd6+Hg4XbOOrnM6Wn/2cfKhnEcozzR4jodrP4qHo+dVt3u1yZsEuAUxdec3TNz6KV5O3rQu15YRTd/G08mrwI5RRIqnJUcX4e3kTYewO65aZmiD52643gvpF+g2pxMu9i6832ocrg5ufL19Ej3m3skfff+mqm81NsZs4Knlj/Fio1doVrYFZ1JO8876Nxm87BHm9lpMclYS9y7sQ4uyrZnSeRrZpizGR39E/wV92DJol35HXacSkRC3b9+e9u3b57suNjaWqlWr5loWEHBp/M6ZM2eIjb10w0twcHCeMjExMbcUl719iRpxUmzZ2Rlz/V+SGI2XZiW42nsh2OPSt/fzmXHY2xsZvfEdKvtUYVaP2dgZ7QBoEtKEZjMaMWv/DB6rN5ivdn5BhimD33svJNAtEID6QQ2445d2bD23iZp+taz7trc38s2uyeSYs/m15xz8/3/sWtUyVbnjl3bWMocSDjJjz/e81fwdnm30AgCdKnbCzs7Ip9Ef83i9J/B1uT03gxRE+1osFn76ax+74nYR6zsbk0OSdV1yzkYyLvRmxnJ7nu0XgTGfmSJeb/4GrzfPvwc4P5M7f51n2RP1B/NE/cE3dwClXEn+DMt/U/vC8eSjVPCqiKND7rTJZDZhwZJrmb3xUpnLv4qu/PtgZ2ew/m9vb+SbnV+QkBHP0kFbKO8ZCkDnip1pOj2CsZtH833XGWw6ux4Xexeei3zBOqzLz82PLWc3Y2dn4FDiAc6nn2dwg6doWrYZANX9qvP9ru9IM6fia+9zzWNT+15SIhLia8nIyMDR0THXMienS71EmZmZpKenA+RbJjEx8ab3azQa8PFxu+ntJS9PT5eiDuGGubhcel9d7b3gfv7SLy8Pdxec3A1sjo3i5eYv4+3jai3TwKs2NfxrsDZ2NS+3fYFNZzfQrHwzqpf7301zPj6VOPnCCQCOXTwGgJubEz4+btbyVUP+N5yok09bQr1CcXS0x8fHjejDG7Bg4Z76ffHw+l8vav96/fh401h2Jm2hV9leBXFKrupW2vfomUT2x8RywWcFOKdgd+XtD3ZpxPss53BcOc4nZ1EtzLcAopWbURI/w3L9bLl9DUawszfm+V1fbnw5TiefzrXs6LNHCfcOx97+UqfHldt4JF46hx4eLvj4uLE2Zg31g+tTs3yVK2pwolu1rszYMQMfHze6VO/EqPXv0PrnptxT8x66VulK33o9uMfYG4Dm7pH4u/ozYOE99K/Vn65VutKhYgc+qzDhho7RltsXSkFC7OzsnOfmuMzMTABcXV1xdr6UkGRlZVl/vlzGxeXmG99stpCUlHbT28v/2NkZ8fR0ISkpHZPJXNTh3JD09EvvvYSE1HzXH4g9AoCnoQxHY09jtpj5cO2HfLj2wzxlHQ1OJCSkci7lPCGB5a9aZ+L/v+9SUzNJSEjlfOoFQtzylvd3DiQrK4eEhFROxV+6GlJrUq186zx09igJgfnv71YVRPtu3h1DUs45MhxOgCnvepN9DEmmGKL3xBLgee1hE1LwSvJnWP6b2hdCXMsTfXYT8fEpueYr/7n7bLJMl/4O/HF0CWOj3icxMY0ESyo5OZd+WV35uzk5Od36/+Xf90cSD+PwnkO++z0dd47q7nWZ1WMOk7Z+zrh14xjzzxgCXQN5ttELPFn/GcDAwr5/8HHUWH7a9TNfRn+Jq70rd1fvz/utP8r3ZuErleb29fR0ue6e7xKfEAcFBREXl3us5OXXgYGB5OTkWJeFhobmKlO9evVb2ndOTul64xQ1k8lc4s6p2XzpUtnV4l55/G/cHNyp5VOXHHM2BgwMrvcMfarkndPWxd6VnBwzno6enEs7n6fONadWUd4jFKPBaN13To4ZHydf4lLj8pSPz7hgLeNuf2l87JyeC3F3cM+z7xD38rf93N9K+2ZnmzEZsi5dmLTkXybHkElWtqnEvYdKkxtp4zWnVtF3fneq+lTjn/s23fC+rnc+bik4JfF3dEHpHN6VP44tYfWJ1dab4QCqede0/rz73G7g0t+DnBwzWAzkWHJynbOkjGQATCaL9fd987ItGdl8VL77tbM4kJNjpk1Ie9qEtCctO41/Tq/i6x1f8vrqV2jg34iIwEgqeFRmYoevMZlNbInbzK/7f+b7XVMo7x7OsIbPX9cx2nL7Qgmbdi0/kZGRREdHYzL9r9to/fr1VKhQgTJlylC9enXc3d3ZuPF/NyMlJSWxZ88eGjVqVBQhSzFmMps5ciaJXUcvcDw2GbPlKtnXdfj3dF7ujh7U9a/PoYQDuabvquZbg482vc/aM2sAaFK2OdGxUZxLO2et60L6Be5b2Jc/ji3Os5/W5dqwKXYjMSlnrMv2x+/jeNIx6+vmZVsCEJ9+Ide+4zPieX/jeyT8/8Mniit/HxdczGWwy/HId73R5IqrOYiAK4aiSPE2c+90avjW4kDCftad/qeowxEbk5qRze5j8ew4fJ6z8f99tbdPlbup4l2Vl1Y9y9mrPIxnX/zeXK89HD04k5J7OEVUTO4bo5uVbcmhiwep5F051+/m3w7MYsaeadgZ7Xh77Qg6/9YWi8WCq4Pr/89MdCmBPpNymgWH51LjuwqcTTuLndGOyKAmjG0zAS8nb86knLqR02LTSnwPcd++ffn2228ZMWIEjz32GDt27OCHH37gnXfeAS6NHR44cCDjxo3D19eXkJAQPvroI4KCgujUqVMRRy/FSfT+OP6IPkzMxQRyLNk4GB0p71uGro0rU6vCtcelXu90Xq83eYv7F/XjyWWP0q/qPZgsZiZt+4wtZzfzfMTLADxZ9xl+2TeT/gt783zESzjZOfFJ9McEugVxT7X7SM5KzrXvJ+o9zY97p9N/YW9ejnwds8XEmI3v4mj837j5GmVq0q9qf15YOYwTySeoH9CAQwkHGbPxHUI9w/N94ERxUivch0CPMpxLbMpF32Vw5X1zFvBIbIy/qy91Kmr8cEmQlJnI4qMLGNPyI77cPpEfdk+heUjLog5LbEBWtomlUcdZt/c4SZmXOj1c7F2oHhJEzxaVCfLN/0u1q4Mr3985k0GL+9NmVlMeqPkIjYOb4Gzvwr4Le5i1/ye2n9tKh9BOlPn/KTQ7hXdh6bHFvPHPK9xZ4S42xqznlwM/5ar3qfpD+PXAz/Sb34On6w/Dx9mXeYfmMH3P97zX4n0A2pRvy5fbJzL0ryfpV7U/2aYsJm77FB8nH1qGtCbLlIXJYuKhJfcxtMELeDh6MPfQHJIyE7mrUs/be0JLkRKfEJcpU4Zvv/2W0aNH07t3b/z9/Rk+fDi9e/e2lhk2bBg5OTm88cYbZGRkEBkZyZQpU/LcaCe2658dMfyydhunjRtI89qJyT4R+2xfYhLqE/NnIg90aED9yld/EMb1TufVLrQDs7r/zrhNH/DoHw/gYHSknn99fu0xn0ZBjQEI8SjHwj7LeHf9mwz762kcjQ40D2nFpI7f4OPsmych9nUuw4Lef/Dm2lcZ9tdTuDm4MaTBs8w7NCdXuc/aT+bTLR/zw+4pvL/xNP4uAfSq3JfXmrxpnfGiuHKwt+Pu1jVI+TODkxdcSHHbTo5DAnbZXnik1iPE2IC+rWri7Fjif6XZhDkHfyPTlEmHsE6cTz/HR5ve53z6+VxzcZstZsZuGsNv+2cRmxpDkFswvav0Y3jk6zjY5R1vabFYGPbXU8w9NJsf7pxJ+1B1eEhuOSYz05ftZePRPZx1WUNGmYOYDVk4ZZbn3MkmnFmQxDM9Iwjwzv/+oio+VVlxzz9M3zOVeYd+54fdU0jNTiXILZimZZvzTvPRub7Y3V99EMcSj/Lzvh+ZtnsqzUNa8u0d07jr9/+9N4PcglnUZxmjN7zDy6ueI9OUSUWvynzS7gvurzEIgPahnZjc6Vsmbfuch5cOxICBJsFN+b3XYnycL3UCzLrrd96Peo/n/36G9Jx0qvvW4LsuM2gZ0vo2ntHSxWCx3MI1YRtmMpmJj789NyHZGvv/v3M3ISG1SMYvJadlMWrmWg6wmBSvqNwrLeCV0Jbazp0YMaAZDvbFO3EsjgqyfQ+fTuTPzcc4EHOWbHM2DkZ7KgYGcEdEONVCrz21kNw+N9rGnX9ri7eTD7O6/05MyhkaTq/Fa03eyjXW8dPoj/li26e803wMYZ7hRMdtZsyGd3g+4mWGN349zxji19a8xPTd3/P9nT/SMazz7Txcm1PUv6MLyvZD5/n6j/Uc9ZpBtlPuYQ8GswNB5+/hjqqtGdjp1u4vKmlKS/vmx9fXzXZuqhO5VdsOnSch8wKpftvyrjRAsudm4uIj2H0s4Zq9xHL7VQrx4qmQepy7mE5KejZuzvb4e7vkuutbird98XvZGreFrztNBSDYvSxty7dn+p6pDG3wnLUt1535h7r+DbivxkAAmoe0xNXeJd8HqIxaP5Lpu7/nuy7TlQzLVUXtj+GCcV+eZBjAYszmottmth2tRI/Uini66QqyrSnxN9WJ3Kr4pEzSjDFYjFn5rjfbp5BpuEh8UkYhRyZX4+/tQoVgTwJ8XJUMlzA/7p2Gu4MHzUNakZh5kcTMi/So1JvjScf4++QKa7kWIa1Zfepvuv/emcnbJnIw4QCP1hnMPdXuy1Xfdzu/5rOt4+lZuQ93hN9Z2IcjJUhMQhIZjmeuuj7LMZbMnEwSkjMLMSopLpQQi81ztDdib3G76nReWIwYLU442PhTfERuVbYpm9kHZpGSnUzt7ytTZUooVaaE8uzfTwPww+7vrGWHNHiW91uNIz0nnZHrRtDip0a0+bkpq0+tzFXnrgs7aR/akTkHf2XHuW2FeDRS0rg4OmA0Xf35A0azK0aDEUcH/a63RWp1sXnVQn3wNATjkBWc73rn9Ap42PtSLUxjVEVuxR/HlnA+/TxjW0/g956Lcv3rU6Uffx5bYp0+0Ggw8midJ1h+92p2PXSIz9pPJsOUwSNLB1kfhADwauM3+L7LTMp7hPL830PJMecU1eFJMVe/YjC+2XUxmPMfLeqaWoNyvr4EXmWmCSndlBCLzQsP9qB6cDCBiV2wz86d9DpkBhKY0omGFctd9c5jEbk+P+2bTpBbMA/UepgWIa1y/Rtc9xlMFhMz9v4AQNfZHRmxZjgA/q7+3Ft9AI/WfoKkrMRcM60EuAbibO/MB60/Zuf57Uza9nmRHJsUf5HVAwh2C8EnvgsG0xVPb7MYcE2uS7Apkvb1wzFqGJZN0k11YvOMBgMDO9Uka6kJl9gyxBv3k2WXgIspAB9LZeqGhtK3dZX/rkjExuSYzOw5Fs+OI+fJyMnGzdGR+pUDqFbeG6Mxd1JxNjWWv04s57G6T1qftnilBoERVPWpxo97pvFCxHCal23JpO2f4e8aQGRQE2JSzzB5+0Sal21JGZcypGan5Nq+fWhHelbqw7hN73NXxe5ULOZza0vh83Z34pHO9fj+TwOxFyoSb7cPsyETr5zKeNsH07VxdRpW9S/qMKWIKCEWATzdHBnSqwF7joWx7VBlEtPS8XV3pWGVAKqF+uT54y5i65JSs5i6dBf7Y0+TYDxAjsNFnLL9Wbu/EvVCQxl0R41c80LP2v8TJouJvlXuvmqd91S7n1Eb3ubP40t5tckbONo5MnPvdD7e/CEejp50Ce/KG81GXnX7US0/4K+Ty3lh5TB+77lIN1xKHhWCPXmlfxO2HjzPvpPVyDGZCCnjRaPqAVd9KIfYBs1DfJM0D3HBKc1zIIratzQyWyxMnredzae3E+s9F5NTPHZ2RkwmM/YZQZRN7EW7qg0YYGPzuZZW+gyXbqW5fW9kHmKNIRYRkRtyLCaZ/TFnOOu1hBzH+Fzrsp1iOeu2nOgjJzl3Mb2IIhQRuTFKiEVE5IbsO5FAkiWGbMeYfNdnuB4hJSeB/ScuFm5gIiI3SQmxiIjckKxsE9mGVLjaEF2DGZMhk6wcU6HGJSJys5QQi4jIDSnj6YybORiD2SHf9XY5HjhZvCjj6ZzvehGR4kYJsYiI3JB6VfzwdvTDLble3pUWcE+KIMDdl5rhepiNiJQMSohFROSGeLo60r1xNcpnd8QzvjX2Wb5gtschMwDv+DsIpSW9WlTHwd6uqEMVEbkumodYRERuWMu6wTg52vHnFm/OJjbDbMnBzuBAOd8ydG1cidoVyhR1iCIi100JsYiI3DCDwUDjGoE0rOrPqXOpGB3sMJjNhPi56dG3IlLiKCEWEZGbZm9npHI5r1I7sb+I2AaNIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKYpIRYRERERm6aEWERERERsmhJiEREREbFpSohFRERExKbpwRwiIiIl1NioMYzb/EGuZXYGO7ycvGgS3JzXmrxJdd8atz2OtafX0HteN37vuYgWIa1u+/5ECpoSYhERkRJucZ/l1p9NFjOnkk/w/sb36PF7Z9bct4lA18AijE6k+FNCLCIiUsI1Cmqc63WT4KaEuJejx9wuzNr3I8MavlBEkYmUDBpDLCIiUgrVC2gAwMnkk8Cl4RUBkzzzlAuY5MnYqDEAnEg6TsAkTyZvm0iLmY2oOiWUmXtmALAtbiv3LuxDpW/LUeO7Cjzx50OcTj6Vq66DCQe4Z0Evwr4OpNbUyry3/m1yzDnW9RfSL/DK6hdoOK0WIV+WoeqUUB5ccj8nko5byxxLPMoDi++l2pQwwr4O5M7ZHVhx/M9c+9l7YQ8DFt1NxW9CqPhNCA8uuZ9jiUcL4KyJrVJCLCIiUgodungQgHDPCje87fsb3+WZBs8yvu1EWpdvw46zO7jz146kZafxWfvJfNTmU3ae38E9C3qRbcq2bvfW2tdoGtycGV1/4a5KPfh86wR+2D0FAIvFwoBF/Vh58i/eaDaSX7rP5cVGr7D61EpeXDkMALPFzMDF95CancoXHb/mhzt/wtfZl0FL7uVI4mEADl88SLc5nTiffo7P2k/mk3YTOZ50jLt+v4Nzaedu9bSJjdKQCRERkRLuyl7Y9Jw0dl/YzRv/vIKnoxf9qvW/4fq6VezB/TUGAWBvb+SJ5Q/h4+zDL93n4mzvDECIewiP//kQey7ssm73eN2neKHRcABahrTmz2NLWXNqNY/WGczZtFhcHdwY2Xw0Tcs2B6BFSCuOJx1j2u6pAJxLP8eBhP08F/ESHcM6A9AwIIKPNn9AZk4mAB9t+gBne2d+6zEfD8dLPd6tyrUhckY9vtj2KSObj7rh4xVRQiwiIlLClf3SN8+yaj7Vmdplxk3dUFejTK1cr9ccX0PHsM7WZBigQWAEmwftBC7NMgHQNLiZdb3BYKC8RyhJWYkABLkFM6fnQgBOJZ/kaOIRDiTsIyp2I1nmLAACXAKo5lOdF1cOY9XJv+kQ2ol2oR14r8X7/4vl1CpahrTCxd7V+kXAw9GTpsHNWHXy7xs+VhFQQiwiIlLi/dlvpfVnB6MjAa6B+Lv633R9Aa4BuV5fSL+Av8t/1+fq4JbrtdFgxGwxW1//dmAWoze8w+mUU/g4+VDbry4u9i7W9QaDgV97zGP85rEsOrKAWftn4mB0oGuF7oxtMx4fZ18SMuOZe2gOcw/NybN/Pxe/Gz1UEUAJsYiISIlXP6Dhf5YxGAwAmMwm7Ix2AKRkp1xX/d7O3pxPP59n+Yrjf1LLr8511bEhZj1DVgzmsTqDebr+MMq6hwDwzro32Riz3louyC2YsW0m8GHr8ey6sJOFh+fy2ZYJeDv78FGbCXg5etG6XFueqj80zz7sjEpr5ObonSMiIlIMJKVmsfXgOeIS0rGzM1I5xIua4T7Y2xXM/e8ejh4AnE45RahnGABRMRuua9tWoa1YcXwZmaZMnOycANhzYTf3LerH911m4uXk9Z91bIrdiNliZnjk63j+f3mT2cSqU5eGOZgtZqLPbuLBJffzY9dfaBAYQR2/utTxq8uy439yJuXSjBbNyrZkf8J+avvVxf7/E2CLxcJTyx+jgldF6vjVvYGzInKJEmIREZEitmF3LL+v20tC1jkSjcextzjjtTOcUN8AHupchyBf11veR6ewzry19nVeWDmMoQ2eIyb1DOM2fYC7g8d/bvtm6zdpNqUZ9y/sxxP1nibLlMkHG0dRz78BHcI6sTk26j/raBgQAcCra17i/hqDuJhxke92fc3u85fGIadlp1LHrx6u9q48s+IJXo58jQDXQFaf+ptd53fwRN2nAHix0St0m9ORAYvu5qHaj+Fk58S03VNZcnQhUzpPv4UzJLZM066JiIgUoV1HLvDzmu0cYCnH/L4m3m8hcf6/ccTnG3YlbObbxdtISc/+74r+QyXvKkzs8BWnU04yYNHdfLV9EuPafkqQW9B/btsguAHz+yzBbDHzxJ8P8crqF4kIimRmt9+sPcb/pUVIKz5o/TGbYjdy38K+vLX2NULcyzG1y48AbIhZh7O9M790/51qvjUY8c8r9F/QmyVHFzGuzafcW30AALX8ajO/91IMBgPPLH+CR/94gLNpsfxw50/cVanHzZ8gsWkGi8ViKeogSiKTyUx8fGpRh1Eq2Nsb8fFxIyEhlZwc839vICWK2rf0UxvfPIvFwiezt7D+3B8klPkDDLnXG03OhF14nIEtm9O6XtkiiVHtW7qV5vb19XXD7jqHHKmHWEREpIjEJaRz/Px5Ut135UmGAcx2GcTb7yD6YEzhBydiQ5QQi4iIFJH0rBxyzDmY7JKvWibHPpnUzIxCjErE9ighFhERKSKero442jlin331+XMdswPw83AvxKhEbI8SYhERkSLi6+lMzXLBeKU2BEveP8n2Wb6UMdUkslpwEUQnYjs07ZqIiEgR6hwZzuHYOpguZJLouYEch3OAEee0igSktqdW2VDqVipT1GGKlGpKiEVERIpQ+QB3nujakF9Xu3LyQk0yLMkYsMfd3pMGVcvRt1VlHOztijpMkVJNCbGIiEgRqxDsyUv3RHLkdBJnE9KwszNQOcQLPy+Xog5NxCYoIRYRESkGjAYDlct5Ubncfz8GWUQKlm6qExERERGbpoRYRERERGyaEmIRERERsWlKiEVERETEpikhFhERERGbpoRYRERERGyapl0TESlAQ1c8yaz9M69ZprxHKJ+1n0zved34veciWoS0KvA4TiQdp9GMOnzWfjL3Vh9Q4PWLiJQmSohFRArQC42G82CtR6yvx28ey47z2/m+y4/WZU52ToR7VWBxn+VU861eFGGKiMgVlBCLiBSgCl4VqeBV0fq6jIsfTnZONApqnKdsfstERKTwaQyxiEgRWHt6DQGTPFl7eg0AY6PG0HhGPcZt+oBqU8KInFGX+IwLAMzY8wOtfmpMuS/9aDCtJmOjxpBjzslV38LD82k7qzmhXwXQ4ZdW7Dq/s9CPSUSkpFIPsYhIMXEq5SSLjizgqzumEp9xAV/nMnwa/TFjNr7LY3UG826L99l1YScfRY3hTMppPmn/BQB/HFvCo38MoneVvrzV7B12nd/JMyueKOKjEREpOZQQi4gUEznmHEY2H0Wb8u0ASMpMZEL0RzxQ6xFGtxoLQLvQDvg6+fL8yiE8WX8I1X1r8PGmD6jnX58vO30HQPvQToCBURveLqpDEREpUTRkQkSkGKlRppb1581no0jLSaNL+J3kmHOs/+4IvxOAVSf/Ij0nne3nttGlQrdc9fSs3LtQ4xYRKcnUQywiUowEuAZYf47PiAfgvkX98i0bmxpLYuZFLFgo4+KXa12ga9DtC1JEpJRRQiwiUkx5OXoBMLnjt1Tyrpxnvb9LAN5OPhgNRs6lxeVal/D/ybSIiPw3DZkQEbmG84npLNt0kl/+OsTvq4+w73gCZrOlUPYdERSJo9GRmNQY6gc0tP5zMDry3oaRnEg+jrO9M5FBTVh4ZD4Wy//i+uPYkkKJUUSkNFAPsYhIPiwWC39uOsmfWw9wMecsycaTOFm88NoVSuXAIB7qXBtvd6fbGoOvcxmGNHiWD6NGkZKVRPOQVsSknOHDqNEYDAZqlakNwIgmb9Nn/l08tHQAD9Z6mMMXDzEh+qPbGpuISGmihFhEJB//7Ihh/uYdnHJeSprPbjCYATif5UdKbDe+W2JhWJ+G2Nvd3gttrzZ5kwC3IKbu/IaJWz/Fy8mb1uXaMqLp23g6XRpS0bRsc37qNpsxG9/hoSUDCPUM45N2XzBwcf/bGpuISGlhsFx5jU2um8lkJj4+tajDKBXs7Y34+LiRkJBKTo65qMORAlYS2zc7x8SoH9ezM2shyd7r86y3y/ahYsKjPNmlGXUr+eVTg20piW0s10/tW7qV5vb19XXD7jo7LTSGWETkX46cSeJ86kXS3Hfnu97kkECC8SA7jpwv5MhEROR2UEIsIvIv6ZkmTOYcTHbJVy2TZbxIakZmIUYlIiK3ixJiEZF/8XJ3xMHOEYcs//wLWMDVVJYynm6FG5iIiNwWSohFRP4lLMiD8r5+uCc3gHzusnDKCMXHEEbDKgF5V4pIibXm1CoCJnnS8qfIog5FCpkSYhGRfzEaDHRvUpnyxsZ4x3fELtsbAIPZAZfkWpRN7klkpXDCgz2KNlARKVAz906nhm8tDiTsZ93pf4o6HClEmnZNRCQfNcJ9efiOBsxd60rMxQZkWVIx4oinoxfN64ZxV7MKGA2Gog5TCpHFYsGgNi+1kjITWXx0AWNafsSX2yfyw+4pNA9pWdRhSSFRD7GIyFXUrlCGV+9rypCuzXmodWse79CCN+9vSe9WlXCw16/P2+W/Llv/vO9HAiZ5ciLpeKHEcyblNAMW3c3J5BOFsj8pGnMO/kamKZMOYZ3oV7U/i44s4Hx67plkft73I8GTfYg+u4k7Z3eg/Ff+NJhWk8+3fmItc8evbeg6u2Oe+vsv6E3vud0A6DW3Ky+ufJYJmz+i7g/VCPs6kPsW9iUuLY6f9s6g8Yx6hH8dTN/5PfK8z+cenE2nX9sQ/nUwtaZW5qWVz3ExI8G6fmzUGBrPqMe4TR9QbUoYkTPqEp9xAYAZe36g1U+NKfelHw2m1WRs1BhyzDkFdQpLNP1GFxG5Bns7IzXCfWlRJ5hG1QPwdHMs6pBKveJ22Xr1qZUsO/5HUYcht9lP+6bTplw7gtyCuafafZgsJmbunZ6nnNli5vE/HqJ35b7M7PYbTYKb8d76t/jrxHIA7q/xAJvPRnEk8bB1m9jUGFafWsm91QdYl809NJtVp/7mk3YTebfF+6w69Te95t7JNzu/ZGTz0YxpNZbo2E28uvpF6zbjN4/liWUP0zAwgu+6TOelyFdZeGQuveZ1Iz0n3VruVMpJFh1ZwFd3TOW1Jm/i61yGT6M/5sWVw2hdri3Tu87ikTpPMHHrJzz/17DbcTpLHA2ZEBGRYkOXraUo7Ivfy9a4LXzdaSoAwe5laVu+PdP3TGVog+dyDZWxYOHFRq8woOYDADQObsriIwtYdnwp7UM70qdKP95e9zq/7v+ZVxqPAODXA7NwsXflrko9rfVkm7L4vsuPeDv7ALD4yAL+PrmCqAHbCfeqAMDO89v5df8sAC5mJDAh+iMG1niQD1uPt9ZTw7cmPeZ24ed9P/Jw7ccAyDHnMLL5KNqUbwdc+lxNiP6IB2o9wuhWYwFoF9oBXydfnl85hNfihlPWIbzAz2tJoh5iEREpNq7nsvVlUbEbaP9LS8p/5U+bn5sy9+DsXOuTMhN5c+1rRM6oS/mv/Gn9c5M8PX4R02szdMWTuZZdOSTj530/MuyvpwBoNKNOnrJSOvy4dxruDh40D2lFYuZFEjMv0qNSb44nHePvkyvylG8U1Nj6s5OdE2Vc/EjLTgPA08mLbhV78NuBWdYyv+ybSY9KvXBz+N9UjVV8qlmTYYAA10D8XPysyTCAj5MvSVmJAESf3USmKZO+Ve/JFUvTss0p7xHK2tNrci2vUaaW9efNZ6NIy0mjS/id5JhzrP/uCL8TgGVHll3/ySql1EMsIiLFxr8vW38QNYqZe6czrOHzecq+uHIYz0e8TF3/evy870eeWPYwXk7etAvtQHpOOt1/78y59DhejnydMM9wlhxdxHN/P0Nc2lmei3jpuuLpGNaZFyJeZnz0R0zt8iM1r0gypHTINmUz+8AsUrKTqf195Tzrf9j9He1Dc48JdrF3yfXaaDBitvzvsccDajzAbwdmsSFmPU5GR/Yn7OOjtp/m2sbDMe8sNS72rleNMyHz0jjhANfAPOsCXANIzLyYZ9ll8RnxANy3qF++dZ9JPnPV/doKJcQiIlIs3Mhla4AXG73CsIYvANA+tBOHLx7mo03v0y60Az/v+5G98XtY0PtPmgQ3/f8yHckxZzN+81gerPUIPs6+/xnTpR67igDU8atLqGdYQR6yFAN/HFvC+fTzjG09gSo+VXOtm75nKvMO/U5MyhmC3cted53Ny7Yk3LMC8w/NwcHoSAWvijQNbnZLcfo4XepNjks7myfOs6lnCQsOv+q2Xo5eAEzu+C2VvHMn/XZ2RqoEh4M5nw1tiIZMiIhIsXCjl617Vu6T63XXincRfXYTKdkprDv9D+U9Qq3J8GX9qvYnw5RB9NlNt/VYpOhYLBZOxqXwZ9QJFqw9yj87YkhOy7pq+Z/2TSfILZgHaj1Mi5BWuf4NrvsMJouJGXt/uKEYDAYD91UfyJKji1hydCH3Vhvw3xv9h4aBjXCyc2L2gV9yLd8Qs55TKSdpfI2EOyIoEkejIzGpMdQPaGj952B05N11b3H04tFbjq+kUw+xiIgUuZu5bP3vS8d+Lv5YsJCcmURCZkKuS8b/3iYxM7EAo5fiIi0jm5kr9rHzxGmSLXFkG5JxMQfis7EMd0ZWpU29srmuNJxNjeWvE8t5rO6TGA15+wgbBEZQ1acaP+6ZxgsRw28olnurD2DspjFYsHBPtftu+dh8nH0Z2uB5xm3+AAc7B7qEd+NE8nE+jBpFNZ/q10y6fZ3LMKTBs3wYNYqUrCSah7QiJuUMH0aNxmAwUC+wHub0q25uE5QQi4hIkbuZy9aJmRdzjeWMSzuLncEOb2cffJx8OHrFtFeXnU2LBS4lCAAGDJgsplxlUrNTCuy4pPCYLRam/bmHTSd3EeuxlCznE2C49ITJuOS6pK3riJODHc1qBVm3mbX/J0wWE32r3H3Veu+pdj+jNrzNn8eX3lA8we5lqe1XFx9nH0I8yt30cV1peOPXCXANZMrOr/hxzzR8nH3pXqkXrzV+E1eHq48/Bni1yZsEuAUxdec3TNz6KV5O3rQu15a3W76Dl7MXCempBRJjSaWEWEREityVl63/3VPnau/KnIO/MWPvD7wc+Zp1+V8nlnN/jUHApblhFxyeS0RgJC72LjQLacG8w3PYGLMh17CJ3/bPwsHoQMPACADcHT04k3I61/6iYjbkep1fz6EUPwdPXmTXqVPEeM0j2+msdbnFmE2qVzSnE5z5I9qTRtUCrA/WGdbw+Xxv2LzSv8tcOZfwZdGDduVZFpsaw+4LO/nq/8fEX2lur8V5ln3e4cs8y4Y3fp3hjV/Pteyh2o/yUO1Hrxpvfttc9kjtx3mk9uO5ltnrIUOAEmIRESlgZrOF/ScvsuXAWS4kp+Lu4ky9igHUqeiLo4NdnvI3e9l6zMZ3yTHnUM6jHN/vmsKhiwf5rft8AO6tNoCpO7/h4aX3MzxyBGGe4fxxbDEz903npUav4uXkDcAdYV34dMvHfBI9jkZBjfnz2FLWnF6Va/+Xyy46soCOYXfk6cGW4mH74fNc5GiuZPhKae47OXexOYdOJ1IjzCffMgVh5/kdLD26iIWH5xPmGU7XCnfdtn1JwVFCLCIiBSYz28T0P/ey7fhxEjhMun0sDiYvNh2pTiX/sjx6Z118PJxybXOzl60/aTeRd9a9ydHEI1QvU5Ofus22PsTD1cGVub2WMGrD24zdNJrkrGQqe1flk3ZfWHuVAZ6NeJELGeeZtO0zss05dAq7gwntvmDQ4v7WMi1CWtG6XDtGbxjJmlMrmXnXbwV1uqQAJadnkmG8cNX1JvskcszZpGVk39Y4MnMy+HL7FwS7BfP1Hd9jZ8z7JVCKH4PFYrEUdRAlkclkJj7etsfbFBR7eyM+Pm4kJKSSk2Pj876UQmrf0u/KNp65bB/L9mzltOfvZDvHWMvYZXsSmNCTBoENGNanIUaj4Ro1SnFSUj7Ds1cdZu7uP4n1+xnyeXvZZXtT+eLjDL2rBdVCb18PcUlTUtr3Zvj6umFnd31DQjRwRERECkR8UgZRB09w1vWvXMkwgMkhiTjvJRyKi+XgqYtFE6CUavUq++FtCMMxM/8b2NyS6xHo6UulEK9CjkxKAiXEIiJSIPafuEhydiLprgfzXZ/jeJ5ETrDvxMXCDUxsQsWynjQID6NsUg+cUyuD5VKKYzS54JHQnHLm5nSNrIz9dfYYim3RGGIRESkQ2TlmzIYsMJquXsaQQlbO1deL3CyjwcCADtVxdrBn82FfklPjMRnTcTR7U8bFhx4tqxNRLe/c1CKghFhERAqIn5czjnhgl+2NyeFinvUGsz0e5lD8PJ0LPzixCU6OdtzfsRodG4Wy73gCmdkmfNydqF3RF2dHpTxydXp3iIhIgaga6k2wpy/nkiJJ8F2W58Ym15S6eDv4Ub+Kf9EEKDYjwNuFAG+X/y4o8v+UEIuISIGwtzPSt2V1Lv6ZhuGCA8nuW8lxPI9djgeuKbUpa2pGt2bV80y7JiJS1G44Ic7IyMDZWZe7REQkrxrhvjzeJYLFUT4cO1ePLHMW9gZ7Ar18uKNhRSKrawyniBQ/N5wQN2/enK5du9KnTx8aNmx4O2ISEZESrFqoD1XLN+TUuVSSUrNwcbInLMgdO6Pu7heR4umGE+Inn3ySefPm8dtvvxEWFkafPn3o2bMnQUFBtyM+EREpgQwGA+UD3Is6DBGR63LTT6rbsWMHc+bMYcmSJaSkpNC0aVP69u1Lx44dcXR0LOg4ix09qa7glOan5Ija1xaojUs3tW/pVprb90aeVHfLj27Ozs5m7dq1TJkyhc2bN+Ph4UHPnj154IEHKF++/K1UXawpIS44pfnDKGpfW6A2Lt3UvqVbaW7fQnt085kzZ5gyZQoTJkxg06ZNVKhQgb59+7Jhwwa6devGwoULb6V6EREREZHb7oZ7iFNSUvjjjz+YO3cu0dHRODs706VLF/r165frJrvBgweze/du/vnnnwIPujhQD3HBKc3fTkXtawvUxqWb2rd0K83teyM9xDd8U12LFi3IysqiXr16vPvuu3Tt2hVXV9c85erUqcOePXtutHoRERERkUJ1wwnxgAED6NevHxUrVrxmuYcffpinnnrqpgMTERERESkMNzyGePjw4f+ZDAO4ublhZ2d3U0HdDmazmc8++4xWrVpRr149HnnkEY4fP17UYYmIiFyXsVFjCJjkSfmv/EnOSsq3zPe7phAwyZOI6bULOTqRks1mZkmfNGkSP//8M6NGjWLWrFkYDAYef/xxsrKyijo0ERGR65Ztzmbp0cX5rpt7aHYhRyNSOthEQpyVlcV3333H0KFDadOmDdWrV2fChAmcPXuWZcuWFXV4IiIi161xUFPmH/49z/LY1Bg2xKyjtl/dIohKpGSziYR43759pKam0rRpU+syT09PatasyaZNm4owMhERkRvTs3IfVp78i6TMxFzL5x/6ncreVahVJu9wibkHZ9Pp1zaEfx1MramVeWnlc1zMSAAgKmYjAZM8WXJ0Ua5tDiYcIGCSJ3MPzgEgIyeDd9a9Sf0falDuSz/a/NyMuQfVIy2lg00kxLGxsQAEBwfnWh4QEEBMTExRhCQiInJTulfqRY45J08C+/uh2fSu0i9P+fGbx/LEsodpGBjBd12m81Lkqyw8Mpde87qRnpNO4+AmVPCqyNyDv+XabvaBWXg6etGlQlcsFguDFt3HD7u/48n6zzCt689EBjXhiWUPM2vfzNt6vCKF4YZnmSiJ0tPTAfI8UtrJyYnExMT8Nrku9vY28X3itrs8R+D1zhUoJYvat/RTGxcOo9EAQFnPIFqEtGThkXkMqD0QgJNJJ9hydjNfdv6G8Zs+Ai79jbqYkcCE6I8YVOshPm7/ibWu2v616Da7M78emMkjdR/nnur3MnHLp2RZMnB1uDSV6u+HZtOzSm/cnFxZfmQ5K44v49su39On6qWk+46Kd5BhSmP0xpH0r3kv9kabSClKHX1+L7GJd6+zszNwaSzx5Z8BMjMzcXFxuak6jUYDPj5uBRKfXOLpeXNtISWD2rf0UxvfXi4ulzp1fHzcGFD/foYuGYrRJQcvZy++3j2fiLIRNKpQD8ft9ta/URsOribTlMkjjR7M9Terq88dhC0PY2PcOl70eY4nmjzKhxvH8E/cX/Sv3Z+o01EcTTzC972n4unpwoqoFRgw0L9BX5ztnaz19KvTh1/2/8zp7KPUD6pf2KdECpCtf35tIiG+PFQiLi6O0NBQ6/K4uDiqV69+U3WazRaSktIKJD5bZ2dnxNPThaSkdEym0vWUHFH72gK1ceFIT780K1JCQiodgrtgtpj5ccss7qsxgJk7fqJftf4kJKSSlZWD2WwhISGVk+cvDQt0MXmSkJD76ap+zgGcS75AQkIqPgTSJLgZ07fO4I6Qu/hu8w+U9wiltkdDkpLSuZB2AQsWPN73yDe2AzFHCHOqcntPgNwWpfnz6+npcvueVFcSVa9eHXd3dzZu3GhNiJOSktizZw8DBw686XpL2yMOi5rJZNY5LcXUvqWf2vj2MpstwKW/PZ4OPrQKacPcA3OI8I9k57kdTLvzZ3JyzP8q5wVATHIsFT1zJ6xnU2MJ8wyztlm/qv15459XiE9NYO6BOdxfYyAmkwWDwYy3szfuDu7M6bkw39gqeFVU25dwtv75tYkBI46OjgwcOJBx48axYsUK9u3bx/PPP09QUBCdOnUq6vBERERuWK/KfVl18m9m7J1G07LNKesekqdMw8BGONk5MfvAL7mWb4hZz6mUkzQObmZd1rNSbwA+iBrF2bRY+lW917quTXgbUrJTsFgs1A9oaP23L34v4zZ9QI7ZdJuOUqRw2EQPMcCwYcPIycnhjTfeICMjg8jISKZMmZLnRjsREZHb7XhsMhv3xrL31FksFguVgvxoUiOYKuW8MBgM11VH14p38fKq5/hq+xeMbjk23zI+zr4MbfA84zZ/gIOdA13Cu3Ei+TgfRo2imk917q02wFrW29mHTmFdmLrrWxoENKSKT9X/7atKV5qXbcEDS+7jhUbDqepTjS1x0XwUNYa2oR0o41Lm1k6ISBGzmYTYzs6Ol19+mZdffrmoQxERERu2ZscZfl+/mwumoyQ67QUsHDpUhc2HK9G5QXXubBJ2XUmxl5M3bcu356+Ty+leqddVyw1v/DoBroFM2fkVP+6Zho+zL90r9eK1xm9aZ5S47O5q97LwyDz6Ve2fa7nRYGRWzzmMWvcun0Z/zPn0cwS5l2VwvWd4sdErN3MaRIoVg8VisRR1ECWRyWQmPj71vwvKf7K3N+Lj40ZCQqpNj18qrdS+pZ/a+PodOp3IFws2ctRhCakeW+CKvNclpTph6T14pFNjGlTxL7og/0XtW7qV5vb19XW77pvqbGIMsYiISHGwfvcZzlkO5EmGAdLd93HWuI01O0+iviqRwqWEWEREpBCYLRZ2nzhLstPePMnwZWkuBzl+7gIp6dmFG5yIjVNCLCIiUkjMZgsW49WTXYsxGzMW69RpIlI4lBCLiIgUAqPBQDk/L5wzQ69axik9FF9XN9xdHQoxMhFRQiwiIlJImtUsh5+pLo4ZZfOss8/yxS+7IS1qhWFn1J9nkcJkM9OuiYiIFLWGVf3Yc6wy5sN3E5cRRYbLYSwGM87pFfDLjKReSBVa1Aku6jBFbI4SYhERkUJiZzQyoFN1QgM8+Wd3IOeTk7FgwcvFnRZ1wmjXIAQnB7uiDlPE5ighFhERKUT2dkbaNSxHq3plOZ+YgcVioYynM45KhEWKjAYpiYiUMrGpMbyz7k2az4wg7OtA6nxflQGL7mb9mbW5yo2NGkPAJM9CianX3K70mtu1UPZVUtjbGQnydSW4jJuSYZEiph5iEZFSZGPMBh5ach++zmV4vO5TVPauwsXMBKbtnkqvuV2Z0G4i99cYVNRhiogUK0qIRURKiYSMeB7/80EqeFXitx7zcXVwta7rVrEHDyy+l1dWv0CH0E4EugUVYaQiIsWLhkyIiJQSv+z/idjUGEa1/CBXMgxgNBh5o9k7PFz7cZKzknOtW3ZsKW1nNaf8V/40/bEBv+z/Kdf6hIx4Xlz5LDWnVqL8V/7cObs9q0+tzFUm25TNR1EfEDmjLqFfBdDqp8b8tHfGVWP968Ryyn3px7C/ntJjikWkyCkhFhEpJf46sRw/F38aBjbKd3113xq822IMlX2q5Fr+0qrneLLeM0y782eC3IIZuuJJdp/fBUBGTgZ95nVn6dFFvN7kLaZ2mUGwWwj3LuzDmlOrrHU8MPcBPtn8MQNqPMCMbr/QMawzz/79dJ7kGmD9mbU8vHQAvar05ZN2X2AwXOU5xiIihURDJkRESomY1DOEeYbd8HYT2n1O+9BOAIR5hdP0xwasO7OGWn61+fXAz+y+sJMlfVcQERgJQIfQO+g1ryvvrX+LP+9exd4Le/h518+833osj9Z+EoDW5dpyKvkka0+v4Z5q91n3tfVsNAMW3UO3ij34rP1kjAb1y4hI0VNCLCJSShgwYjKbbni7JsHNrT+He1YAIDEzEYA1p1YS4BpIPf8G5JhzrOXuCLuTd9a/wcWMBDacWQdAt0o9ctX7Tefvc70+lXKKexf2wWwxM7b1eCXDIlJsKCEWESklQj1DiT67+ZplTiefIsSjXK5lbg5u1p8vJ6lmixmA+IwE4tLOUvZL33zrO5t2lviMeAD8Xfyvue8TScdoW749607/w4ebxvBei/evfUAiIoVECbGISCnRtnwH/ji2hG1xW6gf0DDP+n3xe2n9cxPeaDqSYQ1fuK46vZy8qOhViS87Tcl3fahnGF6OXgCcTz9PoMv/Hjt8KOEg59PP0bTspR7o6r41mNntN8ZGjeHzrRPoU7kfDQIjbvQwRUQKnK5XiYiUEndX7U+AayBv/PMqadlpudaZLWbeXfcmDkYHelfpd911Ni/bgjMpp/Fz8ad+QEPrv9WnVjJx66fYG+ytCe/iIwtybTt64zu8tuZl62tf5zLYG+15vtHLlPMoz3N/DyHblH0LRywiUjDUQywiUgzFXEhly4FzxF1MxdnBgephPtSu4IuD/dWfaObp5MXn7b/k4aUD6DK7HY/WGUxl7yqcSTnN1F3fEn12E593+JLyHqHXHce91QcyZefX3L2gJ882fJFyHuVZdfJvPt86gcfqDMbBzoHa/nW4u+bdvLP2LVKz0qnrX4+VJ/9i8ZEFfNv5hzx1uti78EGrcdy3qB+fb53AC42G39Q5EhEpKEqIRUSKEbPFwtKNx1m+/QAJOTEk2x3H3uzKmv1VCfUN5JE76xLg7XLV7duFdmBpv7+ZvO1zPt/6CXGpsXg7+1DPvz4L+/xJZFCTG4rHzcGNeb2WMnrDSN5d/xbJWUmU9wjljabv8HT9odZyM/rM4NWlI/h2x5fEZ1ygkncVvu38A90r9cq33g5hd9C9Ui8mRH9E90q9qOJT9YbiEhEpSAaLZkS/KSaTmfj41KIOo1Swtzfi4+NGQkIqOTnmog5HCpja98as2X6Gn9dGc9JlMelue8Fw6Ve0XbYn/he7UtO7AS/0i8TJ8eo9xYVNbVy6qX1Lt9Lcvr6+btjZXd/oYI0hFhEpJrJzzKzYfoQYh7Wku++xJsMAJock4nwWcjwhlh1HLhRhlCIipY8SYhGRYuJYbBLnUxJJc9ub73qzfQrxdnvZcSSukCMTESndlBCLiBQTmdkmTBYTZrurD8fKMaaQlplViFGJiJR+SohFRIoJH3cnnOwcccgKyL+ABdxyyhHg5V64gYmIlHJKiEVEiomyfm5UDAjAIyUCLIY8650yQvExhBFRLbAIohMRKb007ZqISDFhMBjo3qwypxcmwgUzSR6byXaMxWB2xiW1GkEZbWhStSIVy3oWdagiIqWKEmIRkWKkQrAnT3RryNy1Hhw/X5tMUwZGgxFvZy9aNAyjS+MwjIa8vcciInLzlBCLiBQzlcp68Xy/CE6cTeZCYgYO9kYqh3jh6uxQ1KGJFImxUWMYt/mDXMvsDHZ4OXnRJLg5rzV5k+q+NXKVjXs6qShClRJKCbGISDFkNBgID/IkPEjDI0QuW9xnufVnk8XMqeQTvL/xPXr83pk1920i0FXj6+XmKCEWERGREqFRUONcr5sENyXEvRw95nZh1r4fGdbwhSKKTEo6zTIhIiIiJVa9gAYAnEw+me/6XnO70mtu11zL1p5eQ8AkT9aeXmNdtil2Iz1+70L410E0mFaTr7dPou/8Hgxd8eTtC16KDSXEIiIiUmIdungQgHDPCjddx8GEA/Sb3wOAr+6YyvDI1/l0y3iiYtYXSIxS/GnIhIiIiJQIOeYc68/pOWnsvrCbN/55BU9HL/pV63/T9X4SPQ53Bw9+vmsOrg6uAFT2qUK3OZ1uOWYpGZQQi4iISIlQ9kvfPMuq+VRnapcZt3RD3T+nV9MprLM1GQaIDGpCqEfYTdcpJYsSYhERESkR/uy30vqzg9GRANdA/F39b7neC+nn8XPJW4+/61Ueoy6ljhJiERERKRHqBzS84W0MGMix5ORalpqdkut1sHtZzqefy7Pt+fRzVPaucsP7lJJHN9WJiIhIoYq7mE7U3rNs3HOWk3EpWCyW27YvD0cPzqSczrUsKmZjrtfNy7Zk+Yk/ycjJsC7beX4HJ5KO37a4pHhRD7GIiIgUiqTULGavPsjOE2dIzU7GghkXO3cqBwVyT5tqBPq6/nclN6hTeBeWHlvMG/+8wp0V7mJjzHp+OfBTrjLPRrzI3EOzuW9hX56qP4TEzEQ+iBqFwWDAaFDfoS1QQiwiIiK3XXpmDl8v2s7uc7s5576KTJ9jgBnHzHJciGlB/MI0hvSMoIyXc4Hu9/7qgziWeJSf9/3ItN1TaR7Skm/vmMZdv/9vBomKXpWYddfvvLP+DR794wH8XPx5tuGLTIj+CDcHtwKNR4ong+V2XqcoxUwmM/HxqUUdRqlgb2/Ex8eNhIRUcnLMRR2OFDC1b+mnNi7dCqp9V207zY9r13Pcdypm+9xjeA1mR8qev58edVrTp3WlWw35hq0+tRJHoyNNyza3LruYkUCt7yszsvkoHq/7VKHHVFhK8+fX19cNO7vr6+FXD7GI5BGfcYEJ0eNYenQRMSlncHVwpY5fPR6q/RjdK/Us6vBEpARav+8UFxy25UmGASzGLBJcthB1sALdmobj5GhXqLHtOLedsVGjGdH0ber61+dC+gUmb/8cLycvele5u1BjkaKhhFhEcknPSafH713INmcztMHzVPKuTFJWEvMOzeHRPwbxXov3GVzvmaIOU0RKEIvFQnxyGtkOeWdyuCzb8RwZKZmkpGcXekL8dP2hZJky+X73FE4nn8LNwY3mIa34vMOX+Ln4FWosUjSUEItILgsOz+VAwn7W3x9NpSumG7qzQjfSc9IZu+l9HqvzJHbGwv2DJSIll8FgwMXRAbts96uWsctxx95oV+jJMIDRYOSFRsN5odHwQt+3FA+6dVJEcolLiwMgv7sLnmv4Ii9EDCfTlGldti1uC/0X9KbalDAqfhPCwEX3sC9+b67tYpJjGLL8Ker/UIPQrwLo/Ftblh5dnKtMwCRPvtv1Dc//PYSqU0IJ/zqYR5YOssYjIiVbZJVy+GTWA0s+Ca8FPNLrUKNcEO4uDoUfnNg8JcQikkv70I7YG+3pM/8uPtr0Pptjo8g2ZQPQIDCCZxoMsz7e9J/Tq+k2pxMmi5lP2k9iQrvPOZ1ymm5zOnEw4QAAcWlnifwmkrWn1vBakzf5rst0ynuE8eCS+/jtwKxc+x6z4V1MFhNfdZrKyOajWH78D97855XCPQEicls0rRlIsEsYvhe6YDT9b3o1g9kRz4utCDbWom398kUYodgyzTJxkzTLRMEpzXe4llQLD8/nldUvcC79Uu+si70LTYObc1/1gfSq0tdarstv7UjOSmb1vRutQygSMy/S5Mf6tAppyzedv2fUxrf5ctsXbBy4hRC3UOu2fef3YH/8XnY8uB+jwUjAJE+aBDdjQe8/rGWe/etp5h+ey9HHzxTSkcvN0Ge4dCvI9j1yJolpy3YQm3KeBONBLOTgba5CGWc/+reuTf0qGq9b2Erz51ezTIjILbmrUg86h9/JmtOrWHXyb9ad+YdVp/7m75MrWHBkHt/e8QNpOWlsjdvCi41eyTWe2MvJm05hXVhx4k8A/jm1hmblmhHmFZ7rl+3dVfsz7K+nOJhwgGq+1QFoFNg4VxzB7mVJy9YXT5HSomJZT169ryk7Dl/gWExtTGYzIf7uNKzqr6ESUqSUEItIvhzsHGgf2pH2oR0BOJsay2trXmbB4bksO76UOn71sGAhwDUwz7YBroEkZiYCl+byrOKXd17RANcAAGs5uNQTfSUjRizoIpZIaeLsaE/jGoE0rpH3d4dIUVFCLCK5dJ3dkco+Vfis/eRcywPdgpjQ7nMWHpnH/oT9tAhpjQEDcWln89RxNi0WX+cyAHg7+xCbEpu3TOql7cq4+N6Go5DSbOiKJ5m1f+Y1y5T3CCV60K4bqnds1BjGbf6AuKeTbiU8ESmBlBCLSC6hnmHMPzSXFyKGE+5VIde6QxcPAlDTtyZuDm7UD2jAvENzcg2bSMpMZNmxpbQq1xaAFiEt+Wr7JI4nHss1hvi3A7MIcA2kglfhP5VKSrYXGg3nwVqPWF+P3zyWHee3832XH63LnOycbrjegTUftF4RERHbooRYpJQymc3sOZbApv2xnEtMxs3ZifoVg2hY1Q9X56uP1Xu9yVusPb2GLrPb8Xjdp2gU2Bg7ox1b47YwedtndAjtRPvQTgCMaDqS/gt6039hHx6rM5hscxafbhlPpimLlyJfBeDpBkP59cDP9P69Oy9FvkoZ5zLM2j+TNadX8Wm7SRgNmuxGbkwFr4pU8KpofV3GxQ8nOycaBTW+xlb/rax7CGXdQ241PBEpgfSXSKQUysw2MXXJHr5cup5lx/5gQ/JsVsbOY/qadYz/bROx8WlX3TbUM4zl96yhb5V7mH3gFx5cch8DFt3N7AO/8HT9Z/nhzp8wGAwAtC7Xlt96zCfLlMngZQ/zwsphlHUry9J+f1HdtwYAgW6BrHtkHfUDGzDin1d49I8HOJV8iml3/sx9NQYWyvkQ2/P19kkETfYmPuOCddkn0eMImOTJXyeWW5etOP4nAZM8OZ50jLFRYwiY5Gld12tuV57/ewifbZlAg2k1Kf+VP11ndyT67KZCPRYRuf3UQyxSCi1Yd4SNx3Zz2ms22U7/G+ObbFpPanwPvltq5OV7InGwz/+JUIGugYxuNfa69tUipBXzey+9ZpkKPhX47s5p15zSJ79xm8Mbv87wxq9fVxwiV+oU3oU31r7KP6dW06NybwDWnF4NwPoza61DI1acWEZ13xqEeYbnW8+Cw/Oo4lOV0S3HYsHCyHUjeHTpA0QP2qWnNYqUIuohFillklKz2Lj/BGddV+ZKhgHMdhmc91nKqYQ4dh2NL6IIRW6/Cl4VqexdhVWnVgKQacpkU8wG6vk3YO3pNdZyK04s446wO69aT445m1+6/07XinfRrWJ3Xmr0KmdST7Pr/I7bfQgiUoiUEIuUModOJ5KclUyG64F815vsk4g3HuTgqcR814uUFh3DOrP61N8ARMVswGgw8kTdp9h+bitp2WkcSTzM0cQj3BF+9YS4mm91PBz/N4zi8hjjtJyrDzsSkZJHCbFIKZNjMmPGhMWQc9UyJjLIyjEVYlQiha9TWGeOJx3jeNIx1pxaRWRQE9qW70C2OZuo2A2sOP4nZZzL0Cgo8qp1uNi75np9+SZQs6V0PdFLxNYpIRYpZfy9XXC2c8Eh6yqT3luMeJkrEOjtmv96kVKiaXBzPBw9WX1qJWtOr6JFSCv8Xf2p5lOd9Wf+YcWJZXQM66yZTkRECbFIaRMW5EGYXwCeyU3Akvcj7pZcD2+HABpW9S+C6ETyl5SWRdTes6zefoYdh8+TlX3rVzAc7BxoU64dfxxdzI5z22gR0gqAluVa89eJFaw/s/aawyVExHZolgmRUsZoMNC3VVXOL0rBeN6BJLdosh3PYWdywzW1JsHmJtzVtAZlvJyLOlQRckxmlm48zurdR0nMuojJkIGDxR1/Nx/ualL1lh/v2ymsM8/+/TRuDu40CIgAoEXZ1kzZ+TWORkfalW9fEIchIiWcEmKRUig8yJOn7opgSZQv+85UJys1CzuDHWW9fenYoAIR1dQ7LMXD3DVHWL57BzEuf5Hutw+LMRtjjjtnk+uTuDIVg6EhkdUDbrr+DmF3YMBA46Am2Bsv/clrEdISAwaalW2Bu6NHQR2KiJRgBovFYinqIEoik8lMfHxqUYdRKtjbG/HxcSMhIfWa89TKzTl/MZ2LKVk4OdoR4u+G8f8fqlFY1L6l38228enzqYz77R8OOf9ChtuhPOs9ElpR17kLI+5vjoO9RvgVFX2GS7fS3L6+vm7Y2V3f7w79hhEp5fy8XahczovyAe6FngyLXMuOQ+e5aIolwzVvMgyQ5rGNuOQE9p9MKOTIRMTWKCEWEZEikZiaRarxNFzle5rJPpkc0klKzSrcwETE5ighFhGRIuHiZI+zpQxcZeCe0eSMHY64OOl2FxG5vZQQi4hIkahdwRcvQ3kcM8vmu94ltSbezt5UK+9TyJGJiK1RQiwiIkWiQllPapcrR1DSXThmhPyvp9hixCWlJsEZbWlftxKuzuohFpHbS79lRESkSBgNBgZ1qoFhOew66UtS8hkyDAm4mYPxsg+gXYPKdGhUrqjDFBEboIRYRESKjKuzA493q8Pxs+HsOhJPRpYJL3dH6lf2w9/bpajDExEboYRYRESKlMFgIDzIk/Agz6IORURslMYQi4iIiIhNU0IsIiIiAOjhtWKrlBCLiIiUQE8te4yASZ78fWJFvuv/OrGcgEmejFz3xnXVN2HzR3yx7bOCDFGkxFBCLCIiUgKN+b/27jwuynL///h7hn1YFGRzF01zS1BRwb3MSstKzWNWtFlZ359a2TlZaWXHNLfU6mSWltliq4Zmi56Wo6nkQpmVeXJfUsAAQRFmYOb+/cFxOnMAI0OWuV/Px4OHM9d13TfXPR8H3nNzzT19ZinaFqO/rb9fp4tPe/SdKj6lv627T20j2unhHo9Wan9PbZmq08UF52OqQK1HIAYAoA4KD4zQ7H7zdSj/gJ7aMtWj78m0x5VRcEzPX7pIAT4BNTRDoO4gEAMAUEcNirtSw1pfp8U7Fmp71jeSpC3HNmvJD4v1t24P66LITpKkIycPa8za29Tm5WZq/lKMhq8cou+Pf+feT/SC0it8zNk2w30bMBMCMQAAddj0PrMVEdhAD61/QMXOYj24/n51iUnUuM73S5KyC7N15YqB+u74dj3VZ44WDnxFLsOlq1MH6eecf0uSPh72mSTpxnY3u28DZkIgBgCgDosIbKBZ/ebpm6x0jfjwGh3I26fnB7woH6uPJOnF755XblGO3rt6pYa3+YsGxV2pd4Z8oMigSM3cOk2SlBjbXZLUMLiR+zZgJgRiAADquCtbDtG1FwzTpqMb9FjPqWpZ/wJ331e//EsdIi9Sw+BGKnGVqMRVIqvFqgHNBmrd4S9rcNZA7cEn1QEA4AUuaTZQqXtW6NJml3m05xTlaH/ePjVaGFHudqeLT8vmZ6uOKQK1FoEYAAAvVs+/nno26q0pPZ8st5+rUAAEYgAAao1Ce4mOZhfIcEmxDWwKCfL70/tMbtRby3e/q1b1L1Co/29XkJi8YaKKSuya03++JMlqYRUlzItADABADbMXO/XZtsPa9NNB5RWdkiFDwX42dWvdVIN6tPhTwfiehLF67+e3dd2qq/V/CeMVHhihlXtW6PWdr2pqr6fc4+r519PWjM1KO7pRSQ17ymKxVMWhAXUCgRgAgBpUXOLSq5/+qPRDu5QVuElF9ffKsLgUUNhcv/6YrINZJ3T3kIRzDsWxwQ310bB/atrXT+hv6+6T3WlXy3oXaP7Fz+uGdinucfd1/Zvmps/SqNXDtWHUVjUJbVpVhwjUehbDMIyankRd5HS6lJPDR1xWBV9fq8LDg5WbW6CSEldNTwdVjPp6P2r853y9M0NLv/xaB+u9oeKA4x591pIQNc25ScO79tSgpOY1Mj/q6928ub4REcHy8ancUiAWDAEAUIPSdh7Rrz47yoRhSXL5nlJ2wDZt2nVIxV4WVoDahEAMAEANcRmGjuWelD3glwrHOAKO6lTRaZ087ajGmQHmQiAGAKCGWCT5+VhlcVV86TOLK0BWi1W+lfzTL4A/jmcXAAA1xGKxKD6ukerZO0oVvKMnuLCdWsZEKtT25y/BBqB8BGIAAGpQcoeGivZtqbATvSXjv34tG5ItP14xRif17dSMy6AB5xGXXQMAoAY1jQ7Rjf3j9dY6q7KPt1Ou307J4lKYo40ifJtqSI92uqhlg5qeJuDVCMQAANSwzm2i1CQ6Wdt2ZWnXkbZyGi61immgbm1j1CQ6pKanB3g9AjEAALVAVP0gDUpqrkGqmesNA2bGGmIAAACYGoEYAAAApkYgBgCTM4wKrvcFACbBGmIAqEPe3vWmxn9xz1nHrBv5tdo1aF+p/c3bNlt+Pv4a2/neqpgeANRJBGIAqIOWXPGmYmwx5fa1qBdX6f08tWWq/pr4UFVNCwDqJAIxANRBF0V2UrMwrkYAAFWhTq0hnjRpkh56qOyZjLS0NA0bNkydOnXSZZddptTUVI9+u92uJ554QsnJyercubPGjx+v7Ozsapo1AFS/U46T6vp6R/Vc1lV2p11S6VrhEauuUfslLZV1OkvRC8IkSXO2zXDfnrVlurq/Ea85W2fowpebq9sbnZRTlK3CkkI9mTZFPd5MUJOFkWq5qLGuW3WNvv91R40dIwBUlToRiJ1Op2bOnKn333+/TN/evXs1ZswY9evXT6mpqRo5cqQeeeQRpaWlucdMmTJFGzdu1HPPPaelS5fq8OHDuvde1ssBqLuchlMlrpIyXy7DJUkK8Q/V/Iuf194TezQ/fY4k6ZUfFmndkS817+LnFW2L1sfDPpMk3djuZvdtSTpy6rA+2vehXrxsiR7u8agiAhto7OdjtGzXa7q3ywN6d0iqnug1TT9l/6gxa2/jTXkA6rxav2Ri7969evjhh3X48GE1atSoTP/SpUvVtm1bd8Bt2bKldu7cqcWLFys5OVmZmZlKTU3Viy++qMTEREnS3LlzdcUVV2j79u1KSEiozsMBgCrR482Ectv7NOmv5Vev+s/tfrqt4x167pt56hbbXVPTHtfN7W/X5S0GSZISY7tLkhoGN3LflqQSV4mm9HxS/ZpeLElyOB065Tip6b1n69rWwyVJPRv31inHKT2+6RFlns5URASfpgag7qr1gXjLli1q166dXnjhBd13331l+rdt26ZLL73Uoy0pKUnTpk2TYRhKT0+XJPXo0cPdHxcXp5iYGG3dupVADKBOen3wO+W+qS7UP9Tj/qPJf9cXhz7TDR+NUFy9lvp7r+mV2n+7Bh3ct/19/PXOkA8kSZkFGdqXt1e7c3/WPw9+KkkqdjrO9TCAOufIycN6cccC/fPApzpWcFRh/vUUH5Wg0ReN0cXNBvzh/W385SsNXXmlPrjmI/Vq3KfKx6Nyan0gHjVq1Fn7MzIyFBsb69EWHR2twsJC5ebmKjMzU+Hh4QoICCgz5tixY1U+XwCoDu0i2lfqTXXBfsEa0upaPfftPPVu3E82P1ul9h9ti/a4/8Whz/Tohoe0+8TPCvELVfsGHRTiX3pW2BBLJmAO64/8S6PX3KwYW4zuSRin1vXbKLvoVy3/+T2NXD1UYzr9n6b2nvGH9tkpKl4fD/tMF0a0PU+zRmXUaCA+cuSIBgyo+NXUhg0bFBUVddZ9FBUVyd/f36PtzH2Hw6HCwsIy/ZIUEBAgu91+DrP+ja9vnViCXev5+Fg9/oV3ob5Vy2q1SCr9+VOZn0G7sn/SSzsW6KLITnp95xKNbDdSSY16ltnnmX399/7P2H9in2795AYNanml3r76fcXVbylJennHS/ri0GeyWqmxN+M5XCqzIFN3rb1VnaO7aNmQdxXoG+juG3rhMC349h+a/NVDah/VQSkdbqn0fsN96yvJllTp8T4+Fve/VZFDqG+pGg3EMTEx+vjjjyvsj4iI+N19BAQEyOHw/HPdmftBQUEKDAws0y+VXnkiKCjoD874N1arReHhwee8PcoKCzv3eqD2o77lO11UrF0Hc+UodiqyfpBaNqrnDqXlCQ4u/WtXvXo2hdc/+8+gEleJxr9/j1rUb6Gv70rTxUsv1rjP79F3d3+nYP/Sba0Wq4KC/N0/z4KCSk8g/PfPt38e/UlFziI9fsmjio+9yN2+7ugXpXMKKd2GGns3s9f36W9fUb4jX68Nf1UN6zUo0z/pkolavT9Vc7fN0rhe98hisejW1Fv1rwP/0oH7DrjHHThxQHHPxGnJNUt0a0Jp/8VLL9aXt3yp/i36S5LSj6Zr8peTtenwJvn7+GtA3ADNHjhbTes1VWheaR1CQ4MUHh4se4ldQ94aom1Ht+mzmz9Tl4Zdzun4zF7fGg3Efn5+atWq1Z/aR8OGDZWVleXRlpWVJZvNptDQUMXGxurEiRNyOBweZ4qzsrLKLLX4I1wuQ/n5p895e/zGx8eqsLAg5ecXyul01fR0UMWob/mcLpfWbjmsf+3Yp9zCPDkNlwJ9AtQ8KkpDe7dR6yb1y92uoKD0L1tf7UnTz7b95Y5pGtZMscGxmr1lhtKPpuuj69bIfsrQnL7P6JJ3+ui+jyZoVv+5kqR6AfW1bt9X+ihyjZIb9VJhYekJhNzcAvf+Wtnaytfqqwmf/FX/r8t4OZx2vbnzdX209yNJUmZutlqGt6TGXorncKkPdqYqPqqzQlwRHs+P/zYkbqgmfTVR637epPjoBDkcJXK5DI/xef/JDgUFduXmFujkyUJJ0smThcrNLdCPv/6gS9/ppy4xXfWPSxfKMAxN3TRFA5Zeqg03bPYYfzw7T7d9fJO2/LJVHwz9UHGBF1Y4t4p4c33DwoIqfea71q8h/j2JiYnasmWLR1taWpq6dOkiq9Wqrl27yuVyKT09XcnJyZKkffv2KTMz033ViXNVUuJd/3FqmtPp4jH1YtT3N4Zh6P11e/T5jz8oI3C9Chv8JJe1UH6OWGUeT1TGh/kaM7irWjWuV2Zbl6t0ve7NH91Q4f4fTy69QsScLTN1S4fblRidpJISl9qGd9Dd8WP1/LfPaFCLIerTpJ/u6/JXzU2fpb+sHKYNo7a69//ftWoWEqcXB76i2Vuf0o0f/kX1A8PVNaabUq/9WNemDtbGwxuU3LIbNfZyZq/vofxDurT5wLM+Bs1DW0iS9uceUIeITuU+n87cdrkMlZS45HSWjnE6S+/P2jxD9QPC9c5Vqe5lGbFBjXTn2lu1I3OHe3xxiVP3rLlLXx35Su8NSVXHiPg/VR+z17fOB+KUlBQNHTpUc+bM0dChQ7Vu3TqtWbNGixcvllS6LOPKK6/U5MmTNX36dAUFBenxxx9X9+7ducIEgBpxKPOUNvy0V0dCVslu2+duLw7IULb/R1K2tCqtnu4b3kUWi+fyievb3qjr295Yqe/zy91lP4DoseS/67Hkv7vv35MwVvckjHXff7D7I3qw+yNlthvS6loNaXVtmfbM/8vj/RQwDYsqXs4klS5BkiRD5x4svz66SQObX+6xRrlzTFdtS/leUulVJiRpatpj2n78W01IfFCdY7qe8/dDqTr/U6x169ZasGCB1q1bp2uvvVbvvfeeZs+e7T4bLElTp05VcnKyxo4dq9GjR6tly5Z69tlna3DWAMws/ecsnTAOyx60r2ynxVB+aLoOHM/S4axT1T85AOVqGtpUB/MPnHXMmf7GIU3O+fvk2nMUGXT2CwpI0u4Tu9WrUR8t2rFQR0/9cs7fD6Xq1Bni119/vdz2vn37qm/fvhVuZ7PZ9OSTT+rJJ588X1MDgErLyjulUz6HVdHJpmL/TNmdDuWetKtZTGj5gwBUq8tbDNZz387T4ZOH1DS0mbv9u6xvFR/dWZK0eu8qRQZFKT6q9L7FYpHT5fTYT0Hx2df41vOvp+yiX8u0f35wrTpE/vam1rn9n1Xvxv3U662umrh+gl4f/M45Hxu84AwxANQ1Qf7+8nVV/MluVqdNPhar/P18qnFWgHm4XIb+fShXX313VGk/ZOjXE4W/u83oTmNUL6Ce7vtyrIpKiiSVfkjH4BWXatDyAZq1Zbo2Hv1KE7r+TT7W0uduqF+ocoqy3eMlaUvG12f9Pj0a9tQXhz6T3fnbpWF3Zv+oUR9dp28y091t0bYYRdmiNClpitYc+ESpu5f/occAngjEAFDNOraIVITrQvmUlH/211bQThG2emrZKKyaZwZ4vz1H8jTz7c16bvUmLd24TkvWbdD0dzbo9bU/6XRRSYXbxdhitPjy1/T98e0a+F5fLf3xFR0+eUjT+8zWzuwfNGfbDLWu30Y3d7jdvc3AFleoyFmk+778P234Zb0W71ioZ9Kflo+l4he7DyQ+qJyibN2w+jqtOfCJPtybqjvX3KL4qM4a0HxgmfE3t79NXWO66ZENDyq3KOfPPTgmRiAGgGp2UcsINY+IVVTuVfIp/q/Qa0iBBW0Ua++ji+NbKoAzxECV2n8sXy99kq7teeu0t95iHYxaqP1Rz2t3wNv61+5vtOTTH1RylkuP9W7cV1/8ZaP6Nb1YC7Y/q5EfDtXMLdOU3KiXnug5XYUlher7dg+tO/ylJKl/00s0pec0bTm2WaNWD9cHe5br1UFvytda8YrVi6Li9cE1H8lluHTX2ls1cf0D6hrbTcuufF8BPgFlxlssFs3uN18n7Ll6dOPDf/5BMimLYRh85uY5cDpdysn5Y9f6Q/l8fa0KDw9Wbm6BqS/54q2ob/mOnyjUK5/s0MGcTOVYf1ax5ZRCnc1U36ex+ndorat7x8lqOfs72msLauzdvKm+C1d9p3VHvtSvkamSxTP++DoaKO7ErbrzsmR1bv37b2orz6niU3rtxyXq0TBJXWO6VcGMzz9vqu//iogINs91iAGgLoqqH6QJIxL1w/4c7TxwoQodxYquH6yubaLUKDK4zOXWAPw5x08U6t9HM3Uy5JsyYViSSvyzlW39SVv/HXfOgTjEL0T/lzDuz04VNYBADAA1xM/XR51bR53zL18AlZdf4JDD5VCxX9nrc5/h8Duu7JNc7tCMWEMMAAC8XmCAr3wtvhW+mVWSfJ1hCg0KrLAf3otADAAAvF7DBjY1i2yg4IKOUjnvnrKW2BRR0lFdLoit/smhxhGIAQCA17NaLBrYJU6N1U0heT1kcfm5+3wdEYrOvUYtwhuxhMmkWEMMAABMoVOrSF1fFK8PNvkr99duOmk9JF/DpjA1VYsGMbr18o4KCiAamRFVBwAAppHUIVbt4yK0ffevOn6ii3ysFrVpWl8XNqsvHyt/ODcrAjEAADCVMJu/+sY3qulpoBbhpRAAAABMjUAMAAAAUyMQAwAAwNQIxAAAADA1AjEAAABMjUAMAAAAUyMQAwAAwNQIxAAAADA1AjEAAABMjUAMAAAAUyMQAwAAwNQIxAAAADA1AjEAAABMjUAMAAAAUyMQAwAAwNQIxAAAADA1AjEAAABMjUAMAAAAUyMQAwAAwNQIxACqhf+yNxQVHXbWL5+fdtb0NM+J9dBBRUWHKeDtN2t6KgCAc+Bb0xMAYC55S96UKyam3D5ni7hqnk3VcMXEKvfjz+Rs0bKmpwIAOAcEYgDVquSiTnI1a17T06haAQEqSexe07MAAJwjlkwAqFUsp04qomtHhffsKtntpY2GoXojrlGD9i1lycr6bYnCB+8r7Ka/KLJ5jCIS2sk2a7rkcnnsL/CNpQrv012RTSIV0bl96ZiSEnd/6Li7VW/41Qp46w2FJ3VWZJNIhffvKf/P1vy2E5dLthlPKiLxotL9JF6k4GlPSMXFkjyXTFiP/qLI2PoKemmB53HlnVBk0ygF/eMZ9z6Dnp2riO7xpd8zqbMCFy+s+gcUAPC7CMQAqpfTWRpI//frP0HWCAnVyfnPy2fvHtnmz5EkBb6ySP7rvtTJec/LiI527yrkwQkyQsOUt+QNFY0cJdvcWQqeMtndH/TM0wp5YLwcffsr7/V3VHj7XbL9Y75C/nqvx5R8v/tWtuef0emJk5S/dJkMPz+Fjb5ZlhO5pft5bp6CXnlJBQ9MVN67qSq8ZbSC/jFftnmzyxyeq1FjFffqq4APlnu0B6xeJRUXyz58ROnc/3a/gmdOU9F1I5X3+juyXz1UIZMfku3pmVXwIAMA/giWTACoVg16JJTb7ujTX3nLV0mSivv0U9Ftd8j23DwVd+uukKmPq/Dm2+W4fJDHNiWd4nXyhcWl21wyUJaCAgW9/KJOP/CgZLEoeN5sFd18uwqmzSodc/EAGRERCr1/rArvHitn23aSJGt+nnL/uU6uuNI1wIYtWPWvGSS/r9bLMeQa+W/aoJJOnWUfdVPpfnr2lmELkhEaVu6xFI0YqbDx98h66KB7eUjAivdU3LufXA0byWfvbgW+8aoKJk1R4fj73XOT1SLbM0+r8LY7ZEQ0ONeHGADwB3GGGEC1ynv9HeWu/VeZr1OznvYYd+rRv8vVqLHq3TBCzoYNderv08vsq+i6kR73HVddI0txsXzTt8p32xZZTp+W44pBHmei7ZeVhmr/dV+4t3NFRrrDsCQ5GzaSJFlOF5Tut1df+a//UvWHXK6gF/4hn90/q2j0GNn/MqrcY7RfdY0Mm00BqStK95OZKb+NX6loROl8/b5aL4thlAb8/5qb4/LBshQVye/rtD/0mAIA/hzOEAOoViXt2lfuTXXBwbIPubb0LHHvfpLNVmaIKybW835klCTJeuKEewlGvVHXlbt7a0aG+7YR9D/7tv7nXIFhSJIKx94rIzhYgW+9oeApkxTy+CMqaddep6bOUHHf/mV3HhIi+6CrFJC6XIXj71dg6vtSYKDsV15duvvcHElSRJ/y34hnzThWbjsA4PwgEAOolXz+vUtBLy1QccdOCnx9iYqG/0UlSckeY84ES/f941mSSoOxxV4kScp/YbGcrS4os39XVHSZtgpZrSoafZeKRt8ly/Hj8v98rWzzZivs9hRl79xb7iZFI65X/euHyWffHgV88L7sg66SQkJKv3dYPUnSiRWrZfyn7b85Gzet/NwAAH8aSyYAnBOXy1BOfpGy84rk/J8rO/xpJSUKHTdGzqbNdGL1WpUkdFbY+LulggKPYf5rPva8/2GqDJtNxV27qbhrNxn+/rIeO6aShC7uL8PPX8FTp8jn0MFKT6f+4EsVPOlBSZIRFSX79TeqaPRdsubnyXLyZLnbFPe7WM6YWAUtWii/b9JVNOL63/p69pYkWXKyPeZmyclR8FNTywR9AMD5xRliAH+I0+XSpu8zlP7dIZ3MzpMkBYeHqUtCM/W+qKF8fc7+Otv3+x1yZWWW2+dq2kyumFjZnnlavt9t14mVn0o2m07OeVbhl/VTyNTHdGrGb2uNA1alyhU9UY4Bl8l/0wYFvbJIBY88JgUHywgO1umx9yp45pOynMpXcc8+sh47quCZ0ySLRSUdOlb6mIt79lbQgmdlREWruFsPWY8dVdAL/5CjZ28ZDRrIUnCq7EY+PrIPG6GglxbIGR2j4n4X//YYtmuvoutGKnTCeJ0+dEglCZ3ls2e3gqc/IWezFuWe0QYAnD8EYgCV5nS59M4/f9aBb3ap2/5t6pS9T1YZ+j48Tl//kqiDR9roxkHtzxqK6912Y4V9px5/Uo5+F8s2d5aKbrndvUTC2fEiFd49VkHPPyP7lVfL2byFJOn0Q5Plt/ErBb22RM7GTXRqxtMqunW0e3+nH3pUruhYBS1ZJNs/npFRr74cffurYNLjMv6zbKEyCh6aLMPfX4HLXpft6ZkyQsNkv2KwCiZPOet2RSOul+2F52Qfep3k4+PRd/LZF2R75mkFLX1Z1qd+kSsqWvZrh6vg4UfLjAUAnF8Ww/jPu0bwhzidLuXkFPz+QPwuX1+rwsODlZtboJKSKv7TO6rUlp8y9c+Vm3XH1nd0Yf4Rj779ITFa2G2U+lyVpN6dGrrbz0d9rYcOqkHiRcp/9gXZr684YKN68Bz2btTXu3lzfSMiguXzO3+1PIM1xAAqLX3HEV10aEeZMCxJcacy1fXAN0r/7pBcvM4GzivOZQFVi0AMoFKKS1z6NStP7XMOVDim3YlDys85qdNFJRWOAcxi4y9fKXpBmDb+8lWV7vffObt05YqBVbrP32N5wqIZX0+r1u8JVCfWEAOoFItFkkUqsVa8vtVpsUoWyWqxnNe5uJo11/Gs/PP6PYDaauWeFdqWuaWmpwF4Fc4QA6gUXx+rGjeL0vboNhWO+TaytaIbRSgogDeFAQDqDgIxgErr0amxdjdtr43R7cv0bY1sox9axKt7p6aynOczxEBddCj/oKIXhGnVng90+6cpilvUSK1fbqb7vxyrU8W/Xbpvx/HtGr5yiFotbqK4RY00fNXVSs/cKkmatWW65mybIUmKXhCmWVtKP9LcZbj07Ddz1f2NeDVZGKmkNztr8Y6FHt/f6XLq2W/mqe/bPdTsxWi1eClWg5dfqq+OrPMYt+mXDRq0fICavxSj5GVd9K9DX57PhwWoFVgyAaDS2jcP1y99OmiFLPrmaDt1yvy59LJrURdob+MLlZDcTgmtI2t6mkCt9td192pU2xQtHbRM32ama/rmv6tBYKQmJ0/RSUe+rl89TL0a9dXLl7+mYqdDc9Nna+SHw/RNyg+6qf0tOlZwVG/+9Jo+HvaZGoU0liT9bd39envXG7q3ywPqFttDaUc3avLGh5TnyNMDiRMlSVO/flxLflikR5OeUPsGHXW04BfN2TpDo9ek6Nubf1KwX7B2HN+uv3x4rXo27q3Fly3VkVNHdOent9bgowVUDwIxgEqzWCwa2K2pmseGassPzfTRwXgZMtSkaaSGd2ykds3DOTsM/I5Lm1+uJ3qVvkGtb5P+WnfkS/3z4KeanDxF/87ZpV8Lf9Udne5Wj4ZJkqQLwtvotR+X6KTjpBqHNlHD4EaSpMTY7pKkvSd2642dr2pS0hSN73K/JOniZgNktVj0TPrTuq3jHYoIbKCMgmN6pMdjuqPT3e65BPoE6fY1N2ln9g/qFttDz3wzVw2CIvX64HcU4BMgSWpgi9AdhGJ4OQIxgD/EYrHowmbhurBZeE1PBaiTzgTZMxoGN9Khk4ckSW0btFdkUKRSPv6LrrlguC5tfpn6NO6nx3tOrXB/Xx1ZL0OGLm8xSCWu367wcnmLwZqbPltfH03T4JZXaeHAlyVJ2YXZ2pe3R3tP7NGn+0s//rzYWSxJ+vroJl3W4gp3GJakqy+4Vj4W3hcA70YgBgCgGtl8bR73rRarDKP0AxFC/EK06to1mps+S6l7lmvpjy/L5mvT8DYjNa33TAX6BpbZX25RjiSpz9vdy/RJUsbpY5Kk7VnfaOL6Cfo26xsF+QbpwvC2ahLaTJJkqPS6xifsuWoQ6Lnsydfqq0gbS6Hg3QjEAADUIheEt9aCSxfJ6XLqm6xteu/fb+vVH19W87AW7iUR/y0soPRjyFdcs1ohfiFl+huHNHWvTW7foKPWX79ZbcIvlNVi1WcH12j1vpXusRGBDXS8MMtje8MwlFuUW8VHCdQuXGUCAIBa4sO9qWr3SpwyT2fKx+qjbrE9NKvfPNULqK+jp0o/IdLnf64F3rNRb0lSTmG2EqK7uL9yinL01Oapyi3K0e7cn5VTlKM7O92jthHtZLWU/vr//NA/JZVepUKS+jTpp88OrtXp4tPu/X9+8DM5nI7zfuxATeIMMQAAv6PQXqLtu3/VrsPZcpQ41bhBmBLbRis2wvb7G/8B3WOT5DScuvWTURrXeYJC/UOVumeF8u15uqrVNZKkev7/OSO8+z11jemmdg3a67o2IzXhX+N16OQhJUR31p7c3Zq++Qk1C2uhVvUvUEHxKYX6h2l++mz5Wnzka/XT6n0r9eZPr0mSTpeUBuC/Jk7UJ/s/0sjVQzW2833KKczWU1v+Lj+rX5UeJ1DbcIYYAICzOJhxUjPf2axX123SJwdWau3R5Vr+7TrNem+j/rn1sAzDqLLvFRMcq3eu+kCh/mG6/8v/pxs/GqHvj2/XK1e8od6N+0qSrmp1jTpHd9G4z+/W898+I0l69pIXdE/CWC398WWN/HCo5qfP0bUXDNd7Q1LlY/VRWEA9vTboLRmGoTvW3qL/9/ldOnLysFZe+6lC/EL19dFNkqSW9S/Qyms/lq/FV3etvVVPb5upv/eeroigiCo7RqA2shhV+Uw2EafTpZycgpqehlfw9bUqPDxYubkFKilx1fR0UMWor/fz5hrnFzg0573N2l24RTnha+XyKSrtMCyyneqoZkWDlHJxorq3i6nZiZ5H3lxfeHd9IyKC5eNTuXO/nCEGAKAC2/6dpWMFR5Ud8clvYViSLIZOh36vYz5b9OX2A3Jxbgmo0wjEAABUYPu+Y8rx+16Gtbjc/tPBP+noiVwdyz5dbj+AuoFADABABQodxXJaCyvsd/kUymk45Sh2VuOsAFQ1AjEAABWIrR+moOJGFfb72WMU4BOg8NCACscAqP0IxAAAVKBH24aKMC6Un71hmT6Ly1f1TycqvkVj1Q8hEAN1GYEYAIAKtGsRrsS4ODXJGy5bfrysziDJsCqgsJmis4epha2DLu/WvKanCeBP4oM5AACogI/VqpsGtlWDzTZt2hWp/OyTchmGAn0DdWHjWF3b+wLFVPGHcwCofgRiAADOws/XR1f3itOArk104NhJOV0uRdUPUsMGwTU9NQBVhEAMAEAlBAf6qUMcn9gGeCPWEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUCMQAAAAwNQIxAAAATI1ADAAAAFMjEAMAAMDUan0gPnbsmCZMmKBevXqpW7duGj16tHbv3u0xJi0tTcOGDVOnTp102WWXKTU11aPfbrfriSeeUHJysjp37qzx48crOzu7Go8CAAAAtVWtDsQOh0N33XWXsrOz9eKLL2rZsmUKDQ3VLbfcopycHEnS3r17NWbMGPXr10+pqakaOXKkHnnkEaWlpbn3M2XKFG3cuFHPPfecli5dqsOHD+vee++tqcMCAABALeJb0xM4m23btunnn3/W+vXrFRMTI0maNWuWunfvri+++ELXXXedli5dqrZt27oDbsuWLbVz504tXrxYycnJyszMVGpqql588UUlJiZKkubOnasrrrhC27dvV0JCQk0dHgAAAGqBWn2GuHXr1nrppZfcYfgMwzCUl5cnqTQ0JyUlefQnJSUpPT1dhmEoPT1dktSjRw93f1xcnGJiYrR169bzfAQAAACo7Wp1II6KilK/fv082l577TXZ7Xb16tVLkpSRkaHY2FiPMdHR0SosLFRubq4yMzMVHh6ugICAMmOOHTt2fg8AAAAAtV6NLpk4cuSIBgwYUGH/hg0bFBUV5b6/du1azZs3TykpKWrbtq0kqaioSP7+/h7bnbnvcDhUWFhYpl+SAgICZLfb/9T8fX1r9euJOsPHx+rxL7wL9fV+1Ni7UV/vRn1L1WggjomJ0ccff1xhf0REhPv2W2+9palTp2rw4MF6+OGH3e0BAQFyOBwe2525HxQUpMDAwDL9UumVJ4KCgs557larReHhwee8PcoKCzv3eqD2o77ejxp7N+rr3cxe3xoNxH5+fmrVqtXvjpszZ44WLVqklJQUTZo0SRaLxd3XsGFDZWVleYzPysqSzWZTaGioYmNjdeLECTkcDo8zxVlZWWWWWvwRLpeh/PzT57w9fuPjY1VYWJDy8wvldLpqejqoYtTX+1Fj70Z9vZs31zcsLKjSZ75r9VUmJGn27NlavHixHnzwQY0ePbpMf2JiorZs2eLRlpaWpi5dushqtapr165yuVxKT09XcnKyJGnfvn3KzMx0X3XiXJWUeNd/nJrmdLp4TL0Y9fV+1Ni7UV/vZvb61uoFI5s3b9bixYuVkpKiq6++WsePH3d/FRQUSJJSUlK0Y8cOzZkzR3v37tUrr7yiNWvW6I477pBUuizjyiuv1OTJk7V582bt2LFDDzzwgLp3784l1wAAACCLYRhGTU+iIo8++qjefffdcvvGjh2rcePGSZLWr1+v2bNn68CBA2rSpInGjRunwYMHu8eePn1a06dP15o1ayRJffv21eTJkxUeHn7Oc3M6XcrJKTjn7fEbX1+rwsODlZtbYOpXp96K+no/auzdqK938+b6RkQEV3rJRK0OxLUZgbjqePOTEdTXDKixd6O+3s2b6/tHAnGtXjIBAAAAnG8EYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmRiAGAACAqRGIAQAAYGoEYgAAAJgagRgAAACmZjEMw6jpSdRFhmHI5eKhqyo+PlY5na6angbOE+rr/aixd6O+3s1b62u1WmSxWCo1lkAMAAAAU2PJBAAAAEyNQAwAAABTIxADAADA1AjEAAAAMDUCMQAAAEyNQAwAAABTIxADAADA1AjEAAAAMDUCMQAAAEyNQAwAAABTIxADAADA1AjEAAAAMDUCMQAAAEyNQIxqc+zYMU2YMEG9evVSt27dNHr0aO3evdtjTFpamoYNG6ZOnTrpsssuU2pqqke/3W7XE088oeTkZHXu3Fnjx49XdnZ2NR4FKmPSpEl66KGHyrRTX+/icrn07LPPqk+fPoqPj9ftt9+ugwcP1vS0cA4WLFiglJQUj7affvpJN910kxISEtS/f3+9/PLLHv3Uv3Y7ceKEHnvsMfXt21ddunTRqFGjtG3bNnc/9fVEIEa1cDgcuuuuu5Sdna0XX3xRy5YtU2hoqG655Rbl5ORIkvbu3asxY8aoX79+Sk1N1ciRI/XII48oLS3NvZ8pU6Zo48aNeu6557R06VIdPnxY9957b00dFv6H0+nUzJkz9f7775fpo77eZ8GCBXr77bf15JNP6p133pHFYtGdd94ph8NR01PDH/Dqq6/q2Wef9WjLzc3VbbfdphYtWmj58uUaN26cnnnmGS1fvtw9hvrXbhMmTNB3332nuXPn6v3331eHDh00evRo7d27l/qWxwCqwcaNG402bdoYGRkZ7ja73W7Ex8cb7733nmEYhvHoo48aI0aM8NhuwoQJxu23324YhmFkZGQYbdu2NdatW+fu37dvn9GmTRvj22+/Pf8HgbPas2ePMWLECCMpKcno37+/MXHiRI9+6utd7Ha70blzZ2PZsmXutry8PKNTp07G6tWra3BmqKyMjAxj9OjRRkJCgnHFFVcYN910k7tv4cKFRp8+fYzi4mJ329NPP21cfvnlhmFQ/9ruwIEDRps2bYz09HR3m8vlMgYOHGjMnz+f+paDM8SoFq1bt9ZLL72kmJgYj3bDMJSXlydJ2rZtm5KSkjz6k5KSlJ6eLsMwlJ6eLknq0aOHuz8uLk4xMTHaunXreT4C/J4tW7aoXbt2Wr16tZo0aVKmn/p6l127dqmgoMCjpmFhYWrfvj31qiN+/PFH1atXT6tWrVJ8fLxH37Zt29StWzf5+vq625KSkrR//35lZ2dT/1ouPDxcL730kjp27Ohus1gs7t+51Lcs398fAvx5UVFR6tevn0fba6+9Jrvdrl69ekmSMjIyFBsb6zEmOjpahYWFys3NVWZmpsLDwxUQEFBmzLFjx87vAeB3jRo16qz91Ne7ZGRkSJIaNmzo0U696o5LLrlEl1xySbl9GRkZatOmjUdbdHS0JOno0aPUv5YLCwsr8zv3k08+0aFDh9S7d2/NmzeP+v4PAjGqxJEjRzRgwIAK+zds2KCoqCj3/bVr12revHlKSUlR27ZtJUlFRUXy9/f32O7MfYfDocLCwjL9khQQECC73V4Vh4EK/NH6lof6epfCwkJJKlOzgIAA9199UHeV93w982LVbrdT/zomPT1djzzyiAYMGKBLLrlETz31FPX9HwRiVImYmBh9/PHHFfZHRES4b7/11luaOnWqBg8erIcfftjdHhAQUGax/pn7QUFBCgwMLHcxv91uV1BQ0J89BJzFH6lvRaivdwkMDJRUWsMztyXq5S3Kez6eeWFqs9mofx3y2Wef6a9//avi4+M1d+5cSdS3PARiVAk/Pz+1atXqd8fNmTNHixYtUkpKiiZNmiSLxeLua9iwobKysjzGZ2VlyWazKTQ0VLGxsTpx4oQcDofHq9asrKwyf4pH1apsfc+G+nqXM39KzcrKUrNmzdztWVlZ7r/6oO6KjY0t9/kqlb5ALikpcbdR/9rrjTfe0LRp0zRw4EDNmTPH/bOV+pbFm+pQbWbPnq1FixbpwQcf1OTJkz3CsCQlJiZqy5YtHm1paWnq0qWLrFarunbtKpfL5X7zlSTt27dPmZmZSkxMrJZjwLmjvt6lbdu2CgkJ0ebNm91t+fn52rlzJ/XyAt26dVN6erqcTqe7LS0tTXFxcWrQoAH1rwOWLVumqVOn6sYbb9T8+fM9TjRQ37IIxKgWmzdv1uLFi5WSkqKrr75ax48fd38VFBRIklJSUrRjxw7NmTNHe/fu1SuvvKI1a9bojjvukFT6qvXKK6/U5MmTtXnzZu3YsUMPPPCAunfvroSEhBo8OlQG9fUu/v7+uummmzRnzhx9/vnn2rVrl+6//37FxsZq4MCBNT09/EnDhw/XqVOnNGnSJO3Zs0crVqzQ0qVLNWbMGEnUv7bbv3+/pk+froEDB2rMmDHKzs52/849efIk9S2HxTAMo6YnAe/36KOP6t133y23b+zYsRo3bpwkaf369Zo9e7YOHDigJk2aaNy4cRo8eLB77OnTpzV9+nStWbNGktS3b19NnjxZ4eHh5/8gUGkpKSlq3LixZsyY4dFOfb2L0+nU3LlztWLFChUVFalbt2567LHHyr3sHmq3hx56SL/88otef/11d9uOHTs0bdo07dy5U1FRUbr99tt10003ufupf+21cOFCzZs3r9y+oUOHasaMGdT3fxCIAQAAYGosmQAAAICpEYgBAABgagRiAAAAmBqBGAAAAKZGIAYAAICpEYgBAABgagRiAAAAmBqBGAAAAKZGIAYAAICpEYgBAABgagRiADCpzz//XBdeeKGee+45d9v+/fuVkJCgiRMn1uDMAKB6WQzDMGp6EgCAmjFx4kR99NFHWrlypVq0aKEbbrhBx48f16pVqxQSElLT0wOAakEgBgATy8/P11VXXaXmzZurT58+mj9/vl577TUlJibW9NQAoNoQiAHA5NavX68777xTVqtVd911l+6///6anhIAVCvWEAOAySUnJ6thw4ZyuVy65JJLano6AFDtCMQAYHLPP/+8fv31V7Vq1UqTJ0+Ww+Go6SkBQLUiEAOAiX3//fdatGiR7r77bj399NPat2+fx1UnAMAMWEMMACblcDg0dOhQWSwWrVixQv7+/po5c6aWLl2qt956S/Hx8TU9RQCoFgRiADCpWbNmacmSJXrrrbeUkJAgSSosLNRVV10lPz8/rVy5UgEBATU7SQCoBgRiAAAAmBpriAEAAGBqBGIAAACYGoEYAAAApkYgBgAAgKkRiAEAAGBqBGIAAACYGoEYAAAApkYgBgAAgKkRiAEAAGBqBGIAAACYGoEYAAAApkYgBgAAgKn9f2Yg0Xq4oA0+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne_plot(for_word='expensive', w2v_model=model, item_range=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "1fb4c8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "e93c855d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_similar_words_arrays(arrays, similar_words, model):\n",
    "    for word, score in similar_words:\n",
    "        word_arr = model.wv.__getitem__([word])\n",
    "        arrays = np.append(arrays, word_arr, axis=0)\n",
    "    return arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "2a26cce6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "similar_words = model.wv.most_similar(['cheap'], topn=10)\n",
    "\n",
    "arrays = model.wv.__getitem__(['cheap'])\n",
    "arrays = get_similar_words_arrays(arrays, similar_words, model)\n",
    "\n",
    "model_pca = PCA(n_components=10).fit_transform(arrays)\n",
    "data_point = TSNE(n_components=3, random_state=0, perplexity=30).fit_transform(model_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "b86c0d1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d') # gca: get current axes\n",
    "\n",
    "x = data_point[:, 0]\n",
    "y = data_point[:, 1]\n",
    "z = data_point[:, 2]\n",
    "\n",
    "ax.scatter(x, y, z, c=x, cmap='Reds', marker=\"o\", label='data points')\n",
    "# ax.plot(x, y, z, color=\"gray\")\n",
    "ax.legend()\n",
    "plt.title('3D projection', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e45e62",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "3ae92c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\megan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.sentiment import SentimentIntensityAnalyzer\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "42ede0c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.0, 'neu': 0.295, 'pos': 0.705, 'compound': 0.8012}"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia = SentimentIntensityAnalyzer()\n",
    "sia.polarity_scores(\"Wow, NLTK is really powerful!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "e57538f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text     label\n",
       "0  Stuning even for the non-gamer: This sound tra...  Positive\n",
       "1  The best soundtrack ever to anything.: I'm rea...  Positive\n",
       "2  Amazing!: This soundtrack is my favorite music...  Positive\n",
       "3  Excellent Soundtrack: I truly like this soundt...  Positive\n",
       "4  Remember, Pull Your Jaw Off The Floor After He...  Positive"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amazon_reviewDF.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "3a2f01e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     C:\\Users\\megan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package movie_reviews is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\megan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'compound': 0.9814}"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('movie_reviews');\n",
    "nltk.download('punkt');\n",
    "\n",
    "sia.polarity_scores(amazon_reviewDF.loc[3].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfea4847",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcfc3162",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ffbb85e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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