This project performs sentiment analysis on tweets related to Palestine using PySpark, TextBlob, and Nitter scraper. The analysis includes extracting tweet data, performing sentiment categorization, and building a Naive Bayes classification model.
- Tweet Scraping: Collect tweets using Nitter scraper.
- Sentiment Analysis: Use TextBlob to compute sentiment scores and categorize them as 'Happy', 'Sad', or 'Neutral'.
- Visualization: Generate sentiment distributions and word clouds for positive sentiment tweets.
- Naive Bayes Classifier: Build a machine learning model to classify tweets based on the number of likes.
- Cross-validation: Perform cross-validation to improve model accuracy.
- Streamlit App: Deploy a web interface to predict sentiment for a user-provided tweet.
- Install the necessary dependencies:
pip install ntscraper pip install textblob pip install pyspark pip install streamlit pip install wordcloud