Tweet Classifier

A web application that lets you classify tweets in different categories using local models (Random Forest, Naive Bayes, and SVM) or using the TweetNLP library. Along with creation of 3 local models and comparison between them.

🛠 Tech Stack :

Languages :

Front-End

• HTML
• CSS3
• JavaScript

Back-End

• Python

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Framework :

Front-End

• ReactJS
• TailwindUI

Back-End

• Flask

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Libraries :

Front-End

• MUI

Back-End

• TweetNLP
• NLTK
• Scikit-Learn
• Matplotlib
• Pandas

✨ Features :

• Input Processing: Users can upload CSV files containing unlabeled tweet data for classification.
• Classification and Comparison: The application classifies tweets using Random Forest, Naive Bayes, and Support Vector Machine models and displays a side-by-side comparative analysis of their results.
• Analytical Tools: Features include sorting tweets by category and filtering by category for refined analysis.
• Output Accessibility: The classified data can be easily downloaded from the web application after processing.
• Ease of Operation: A single batch file is available to simultaneously launch both frontend and backend services, requiring no additional commands from the user.

🖥️ Preview :

🖼️ Architecture Diagram :

📈 Results :

📜 License :

MIT Copyright (c) 2023 Lalit Mangal