CCW/GGE Most Common N-Gram* Expressions Bingo is a tool designed to identify and analyze the most frequent expressions in the United Nations' Convention on Certain Conventional Weapons (CCW) Group of Governmental Experts (GGE) on Lethal Autonomous Weapons Systems statements containing a specified target expression. This project helps in understanding common phrases and sentiments associated with specific terms within a given set of statements.
• Expression Filtering: Filters statements to focus on those containing a specific target expression.
• N-gram Frequency Analysis: Identifies the most common sequences of words (n-grams) within filtered statements.
• Sentiment Analysis: Assesses the sentiment of statements containing the target expression (available but not used for the project at hand).
• Text Tidying: Prepares and cleans text data for analysis by removing unnecessary elements.
• Multiple Filters: Provides several filtering scripts to focus on specific types of expressions, such as those related to ethics, legal instruments, and guiding principles.
• accountability_filter.py: Filters statements based on accountability-related expressions.
• agreed_language_filter.py: Focuses on filtering agreed-upon language within statements.
• ethics_filter.py: Extracts statements related to ethical considerations.
• guiding_principles_filter.py: Filters statements for guiding principles.
• human_control_filter.py: Identifies statements discussing human control.
• ihl_filter.py: Filters statements related to international humanitarian law.
• legally_binding_instrument_filter.py: Focuses on legally binding instruments within statements.
• ngram_frequency.r: R script for analyzing the frequency of n-grams in the text.
• sentiment_analysis.r: R script to perform sentiment analysis on the filtered statements.
• session_word_frequency.r: Analyzes the word frequency during specific sessions.
• statement_tidier.r: Cleans and prepares statements for analysis.
• text_tidier.r: General text cleaning and tidying script.
• word_frequency.r: Analyzes word frequency across the dataset.
1. Setup: Ensure that you have Python and R installed on your system.
2. Running Filters: Use the provided Python scripts to filter the dataset for relevant expressions.
3. Frequency Analysis: Run the R scripts to analyze the frequency of expressions in your filtered data.
4. Sentiment Analysis: Use the sentiment_analysis.r script to understand the sentiment in statements containing the target expression.
• Python 3.x
• R 4.x
• Required Python libraries: Listed in the scripts as import statements.
• Required R packages: Listed in the R scripts as library() statements.
If you would like to make a spin-off project based on this, feel free to reach out.
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