Twitter Comment Scraper and Sentiment Analysis

This project scrapes Twitter comments for a specified post, processes the data by cleaning and translating the text, and performs sentiment analysis using various Python libraries. It generates a CSV file with the cleaned comments, performs sentiment analysis, extracts key elements, and generates a word cloud of the most mentioned topics.

Features

• Scrape a Twitter post and its comments using Selenium.
• Clean and preprocess the scraped data by removing nonsensical data, URLs, and long substrings.
• Translate non-English comments to English using the deep_translator library.
• Perform sentiment analysis on the comments using TextBlob.
• Extract key topics from the comments.
• Generate a word cloud to visualize the most frequently mentioned topics.
• Generate explanations for key topics using langchain and OpenAI GPT-4.

Requirements

Make sure you have the following Python libraries installed:

pip install selenium pandas re textblob wordcloud matplotlib deep-translator langchain_openai scikit-learn

SAMEPLE ANSWER AND SENTIMENT ANALYSIS

sentiment_analysis_summary_sample.txt