I'm thrilled to unveil one of my latest projects - a Sentiment Analysis Web Application, crafted with the powerful combination of Flask and Django frameworks. This application is designed to dive into the world of tweets and extract sentiments - be it positive, neutral, or negative, through a sophisticated logistic regression model.
What's Inside?
- In-Depth Sentiment Analysis: Leveraging logistic regression to accurately gauge the sentiment of any tweet.
- Advanced Tweet Preprocessing: Ensuring every tweet is cleaned and preprocessed for the most accurate sentiment analysis.
- User-Friendly Interface: Built with Flask for ease of use and Django for robust back-end management.
- Visual Insights: Witness the power of data visualization in understanding tweet sentiments.
Tech Stack and Tools
- This project is built on Python 3.x, integrating Flask and Django for a seamless user experience.
- NLTK, Numpy, and Pandas come together to process and analyze data, while Matplotlib adds a touch of visual flair (optional).
Getting Started
- Installation is a breeze! Just a few commands to get Flask, Django, and other libraries up and running.
- Simply clone the repo or download the source code to get started.
Dive into the Application
- Fire up the Flask server and explore the application via your local web browser.
- Discover the nuances of tweet sentiment analysis, from preprocessing to final prediction.
Behind the Scenes
- Take a look at app.py for the core application logic and routing.
- Delve into model.py to understand the logistic regression model at work.
What Sets It Apart?
- Experience a practical application of sentiment analysis in the realm of social media.
- The combination of Flask's simplicity and Django's robustness makes this project not just a coding exercise, but a real-world solution.
Stay tuned for more updates and exciting projects from my portfolio!