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Sentiment Analysis of Amazon Product Reviews

This project performs sentiment analysis on Amazon product reviews, classifying reviews into positive, neutral, or negative sentiments using various machine learning models.

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Project Structure

.
├── data/                         # Dataset
│   └── amazon_reviews.csv         # Amazon product reviews dataset
│
├── scripts/                      # Python Scripts
│   ├── data_preprocessing.py      # Data loading & preprocessing
│   ├── model_training.py          # Model training & evaluation
│   └── utils.py                   # Utility functions (e.g., evaluation metrics)
│
├── models/                       # Saved models
│   └── saved_models/              # Serialized trained models (Optional)
│
├── outputs/                      # Confusion matrix & evaluation results
│   ├── confusion_matrix_Naive_Bayes.png
│   ├── confusion_matrix_Logistic_Regression.png
│   ├── confusion_matrix_SVM.png
│   └── confusion_matrix_Random_Forest.png
│
├── README.md                     # Project documentation
└── requirements.txt              # Dependencies for the project

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How to Run

1. Clone the repository

Open a terminal or command prompt and run the following commands to clone the repository:

git clone https://github.com/sachi143/sentiment-analysis-amazon-reviews.git
cd sentiment-analysis-amazon-reviews

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2. Install dependencies

Make sure you have Python 3.x installed. Install the required dependencies by running:

pip install -r requirements.txt

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3. Run the sentiment analysis pipeline

To execute the entire sentiment analysis pipeline, run:

python scripts/model_training.py

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Output

• Classification reports for all models will be displayed in the terminal.
• Confusion matrices for each model will be saved in the outputs/ directory:
  • confusion_matrix_Naive_Bayes.png
  • confusion_matrix_Logistic_Regression.png
  • confusion_matrix_SVM.png
  • confusion_matrix_Random_Forest.png

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Models Used

1. Naive Bayes Classifier
2. Logistic Regression
3. Support Vector Machine (SVM)
4. Random Forest Classifier

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Next Steps

1. Hyperparameter Tuning: Optimize model parameters to improve performance.
2. Try Advanced Vectorization: Experiment with Word2Vec, GloVe, or BERT embeddings.
3. Deploy the Model: Use Flask or FastAPI to deploy the best-performing model as an API.
4. Visualization: Add more data visualizations for insights into the dataset and model performance.

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Requirements

List of dependencies:

numpy
pandas
scikit-learn
matplotlib
seaborn
joblib

You can install these dependencies with:

pip install -r requirements.txt

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License

This project is licensed under the MIT License. Feel free to use and modify it as needed.

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Contributing

Contributions are welcome! If you find any bugs or want to add features, feel free to open an issue or submit a pull request.

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Contact

For any questions or issues, please contact:

Sairam Chennaka
Email: sachi777@outlook.in
GitHub: https://github.com/ram-cs7

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