Tweet Sentiment Analysis using Naive Bayes

A Machine Learning project that classifies tweets into Positive or Negative sentiments using the Multinomial Naive Bayes algorithm and TF-IDF Vectorization.

This project demonstrates the complete machine learning workflow—from text preprocessing and feature extraction to model training, evaluation, and saving the trained model for future predictions.

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📌 Project Overview

This project performs the following tasks:

• Load the tweet dataset
• Clean and preprocess text data
• Remove stopwords and perform stemming
• Convert text into numerical features using TF-IDF
• Train a Multinomial Naive Bayes classifier
• Evaluate the model using accuracy and confusion matrix
• Save the trained model and vectorizer

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• NLTK
• Scikit-learn
• Joblib
• Pickle
• Jupyter Notebook

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📂 Project Structure

Tweet-Sentiment-Analysis/
│
├── Tweet_Sentimental_Analysis_with_Naive_Bayes.ipynb
├── twitter_training.csv
├── twitter_validation.csv
├── tfidf_enc.csv
├── tfidf_model.joblib
├── model.mdl
└── README.md

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🚀 Machine Learning Pipeline

Tweet Dataset
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Text Preprocessing
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Stopword Removal
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Stemming
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TF-IDF Vectorization
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Multinomial Naive Bayes
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Sentiment Prediction

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🤖 Model Used

Algorithm: Multinomial Naive Bayes

Feature Extraction: TF-IDF Vectorizer

Multinomial Naive Bayes is a probabilistic machine learning algorithm widely used for text classification tasks because of its simplicity, speed, and effectiveness.

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📈 Model Evaluation

The model is evaluated using:

• Accuracy Score
• Confusion Matrix

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💾 Saving the Model

The project saves the trained artifacts after model training:

• model.mdl – Trained Naive Bayes model
• tfidf_model.joblib – Saved TF-IDF Vectorizer
• tfidf_enc.csv – Encoded TF-IDF feature representation

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▶️ Getting Started

1. Clone the repository

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

2. Install dependencies

pip install pandas numpy nltk scikit-learn joblib

3. Launch Jupyter Notebook

jupyter notebook

4. Open and run

Tweet_Sentimental_Analysis_Using_Naive_Bayes.ipynb

Run all cells in sequence.

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📚 Concepts Covered

• Natural Language Processing (NLP)
• Text Cleaning
• Stopword Removal
• Stemming
• TF-IDF Vectorization
• Naive Bayes Classification
• Model Evaluation
• Model Serialization

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🚀 Future Improvements

• Add Neutral sentiment classification
• Train on a larger dataset
• Compare with Logistic Regression and SVM
• Build a Streamlit or Flask web application
• Improve performance using hyperparameter tuning

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👩‍💻 Author

Prachi Sharma

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