💬 TF-IDF Sentiment Analysis on Twitter Dataset

This project performs sentiment classification on Twitter data using TF-IDF vectorization and machine learning classifiers. The notebook includes data cleaning, vectorization, training, and performance evaluation using classic models like Logistic Regression and Naive Bayes.

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📁 Project Structure

TFIDF-Sentiment-Analysis/
├── TF-IDFSentimentAnalysis.ipynb  	# Main notebook
├── NLP TF-IDF Sentiment Analysis.pdf   # Project summary
├── sample_sentiment_dataset.csv        # 5K sample of original dataset
├── requirements.txt                    # Python dependencies
├── README.md                           # Project documentation
└── .gitignore                          # Git exclusion rules

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🧠 Dataset

The original dataset is from Kaggle Sentiment140, containing over 1.6M tweets labeled with sentiment polarity (0 = Negative, 2 = Neutral, 4 = Positive).

⚠️ Due to GitHub file size limits, only a 5,000-row sample is included as sample_sentiment_dataset.csv for testing and demo purposes.

Each record includes:

• Polarity (0/2/4)
• Tweet ID
• Date
• Query
• Username
• Cleaned tweet text

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🔍 Methods Used

• Text cleaning (lowercasing, punctuation, stopwords removal)

• TF-IDF vectorization with TfidfVectorizer

• Machine learning classifiers:

  • Logistic Regression
  • Naive Bayes
  • SVM (optional)

• Evaluation:

  • Accuracy, precision, recall, F1-score
  • Confusion matrix
  • Visual plots

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/your-username/TFIDF-Sentiment-Analysis.git
cd TFIDF-Sentiment-Analysis

2. Install Dependencies

pip install -r requirements.txt

3. Run the Notebook

jupyter notebook TF-IDFSentimentAnalysis.ipynb

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📄 License

MIT License © 2025 Sanjana Shah

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👤 Author

Sanjana Shah
✨ Machine Learning & Generative AI Enthusiast
📫 Connect on LinkedIn GitHub: @shahsanjanav

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⭐ If you like this project, consider starring it on GitHub!