🐦 Twitter Sentiment Analysis

A full-stack web application that predicts the sentiment of tweets using a Logistic Regression machine learning model.
The project uses a modern Next.js frontend and a FastAPI backend, deployed as a monorepo on Vercel.

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🚀 Features

• Real-time Prediction
  Instantly analyze tweet sentiment as Positive or Negative.

• Confidence Scoring
  Displays prediction probability using predict_proba from Scikit-Learn.

• Hybrid Architecture
  Combines a fast React-based frontend with a Python-powered ML backend.

• Responsive UI
  Built with Tailwind CSS and Lucide Icons for a clean, modern interface.

• Random Tweet Generator
  Quickly test the model using pre-loaded sample tweets.

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🛠️ Tech Stack

Frontend

• Framework: Next.js 15+ (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• Icons: Lucide React

Backend

• Framework: FastAPI
• ML Libraries: Scikit-Learn, NLTK, Pandas, NumPy
• Server: Uvicorn

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📂 Project Structure

├── api/                    # FastAPI backend
│   ├── index.py            # Main API logic & ML routes
│   ├── trained_model.sav   # Serialized Logistic Regression model
│   └── vectorizer.sav      # TF-IDF Vectorizer
├── app/                    # Next.js App Router (Frontend)
├── components/             # Reusable React components
├── requirements.txt        # Python dependencies
├── vercel.json             # Vercel deployment configuration
└── package.json            # Node.js dependencies

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⚙️ Local Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/twitter-sentiment-analysis.git
cd twitter-sentiment-analysis

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2️⃣ Setup the Python Backend

It is recommended to use a virtual environment.

# Create and activate virtual environment
python -m venv .venv
source .venv/Scripts/activate   # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the FastAPI server
uvicorn api.index:app --reload --port 8000

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3️⃣ Setup the Next.js Frontend

Open a new terminal window:

# Install dependencies
npm install

# Run the development server
npm run dev

Visit: http://localhost:3000

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🧠 Model Information

• Dataset: Sentiment140 (1.6 million tweets)

• Algorithm: Logistic Regression

• Text Preprocessing:

  • Stopword Removal
  • Porter Stemming (NLTK)

• Feature Extraction: TF-IDF Vectorization

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☁️ Deployment

This project is configured for Vercel.

• The vercel.json file routes /api requests to FastAPI serverless functions.
• Frontend and backend are deployed together as a monorepo.

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📄 License

This project is for educational purposes only.

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⭐ Acknowledgements

• Kaggle – Sentiment140 Dataset
• Scikit-Learn & FastAPI Documentation

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If you like this project, feel free to ⭐ the repository!