This project performs sentiment analysis on Reddit posts scraped from the "politics" subreddit using natural language processing techniques. It utilizes a Multinomial Naive Bayes classifier trained on a labeled dataset to classify the sentiment of the posts into different categories.
- Introduction
- Setup
- Front-End
- Results
The goal of this project is to analyze the sentiment of Reddit posts on political topics. It involves scraping posts from the "politics" subreddit, preprocessing the text data, training a Multinomial Naive Bayes classifier, and predicting the sentiment of new posts. It provides a front-end for users to utilize it and an authentication facility.
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Install Python dependencies:
pip install numpy pandas praw scikit-learn nltk -
Install NLTK data:
python -m nltk.downloader stopwords -
Clone the repository:
git clone https://github.com/eshan1347/DataFeedProject && cd DataFeedProject -
Run the Python script:
python sentiment_analysis.py
A complete frontend for this project has been developed using React.js and Flask. It includes login and home pages for interacting with the sentiment analysis model.
The trained model will predict the sentiment of Reddit posts based on the provided dataset and new scraped posts. The results will be displayed in a multi-class format, where each sentiment category is assigned a numerical value.