🐦 Twitter Sentiment Analysis - Capstone Project

A comprehensive machine learning project that analyzes sentiment from Twitter text data using Logistic Regression. This project includes a web application with a modern, professional interface for real-time sentiment analysis.

🎯 Overview

This project implements a sentiment analysis system that classifies Twitter text as either Positive or Negative. The model is trained on 1.6 million tweets using Logistic Regression with Count Vectorization, achieving 79.99% accuracy.

Key Highlights

• ✅ 79.99% Model Accuracy
• ✅ 1.6M+ Training Samples
• ✅ 684,358 Features
• ✅ Real-time Predictions
• ✅ Confidence Scores
• ✅ Professional Web Interface
• ✅ Model Performance Analytics

✨ Features

Web Application

• Modern UI: Clean, professional design with dark theme
• Real-time Analysis: Instant sentiment predictions
• Confidence Scores: Shows prediction confidence percentage
• Responsive Design: Works on desktop, tablet, and mobile
• Error Handling: User-friendly error messages
• Analytics Dashboard: Visual representation of model performance

Model Features

• High Accuracy: 79.99% accuracy on test set
• Fast Inference: Quick predictions with optimized preprocessing
• Confidence Metrics: Provides probability scores for each prediction
• Robust Preprocessing: Handles various text formats and edge cases

🚀 Installation

Prerequisites

• Python 3.9 or higher
• pip (Python package manager)

Steps

1. Clone the repository

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

2. Create a virtual environment (recommended)

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Download the dataset

   • The dataset should be placed in the data/ directory
   • Ensure the file is named training.csv
   • Format: CSV with columns: target, ids, date, flag, user, text

5. Train the model (if not using pre-trained models)

   python scripts/train_model.py

💻 Usage

Running the Application

1. Start the Flask server

   python MLapp.py

2. Access the application

   • Open your browser and navigate to: http://localhost:5000
   • Or: http://127.0.0.1:5000

3. Using the Application

   • Enter text in the text area
   • Click "Analyze Sentiment"
   • View the sentiment prediction (Positive/Negative) and confidence score

🛠️ Technologies Used

Backend

• Python 3.9+: Core programming language
• Flask 3.0.0: Web framework
• scikit-learn 1.3.2: Machine learning library
• joblib 1.3.2: Model serialization

Machine Learning

• Logistic Regression: Classification algorithm
• CountVectorizer: Text feature extraction
• Train-Test Split: Data splitting (80-20)

Data Processing

• Pandas: Data manipulation
• NumPy: Numerical operations

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

👤 Author

Anuj Patel

• LinkedIn: https://www.linkedin.com/in/anujpatel04/
• Email: anuj.patel.29dec@gmail.com

🙏 Acknowledgments

• Dataset: Sentiment140 dataset from Kaggle
• Scikit-learn: For machine learning algorithms
• Flask: For web framework
• Community: For open-source libraries and resources

📞 Support

If you have any questions or encounter issues:

• Open an issue on GitHub
• Contact: anuj.patel.29dec@gmail.com

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This project demonstrates the application of machine learning in natural language processing for sentiment analysis.