This project is a Sentiment Analysis Web Application built using Flask and Django frameworks. The core functionality revolves around analyzing tweets to determine their sentiment (positive, neutral, or negative) based on a logistic regression model trained on tweet data.
- Sentiment Analysis: Analyzes the sentiment of tweets using logistic regression.
- Tweet Preprocessing: Includes cleaning and preprocessing of tweets for analysis.
- Interactive Web Interface: Built with Flask and Django for a user-friendly experience.
- Data Visualization: Visualizes tweet data and sentiment analysis results.
- Python 3.x
- Flask
- Django
- NLTK
- Numpy
- Pandas
- Matplotlib (optional for additional data visualization)
- Ensure Python 3.x is installed.
- Install Flask, Django, NLTK, Numpy, and Pandas:
pip install Flask Django nltk numpy pandas
- Clone the repository or download the source code.
- Start the Flask server:
python app.py
- Access the web application through the provided local URL (usually
http://127.0.0.1:5000/).
app.py: Main Flask application file with routes and sentiment analysis logic.model.py: Contains the logistic regression model and related functions.templates/: HTML templates for the Flask web interface.static/: Folder for static files used in the web application.
- The application uses NLTK for tweet preprocessing and logistic regression for sentiment analysis.
- Users can input a tweet or select from predefined examples to analyze sentiment.
- The logistic regression model is trained on tweet data, providing accurate sentiment predictions.
- The Django setup ensures smooth management and scalability of the web application.
- The sentiment analysis is currently limited to English language tweets.
- The logistic regression model might not capture the nuances of complex sentences or slang.
- Pianalytix for creating their Data Science Bundle course.