Sentiment Analysis Tools

This tool is designed to solve the problem of sentiment classification and prediction using advanced transformer-based models.

Introduction

Sentiment_Analysis is a Python-based package designed for sentiment classification and prediction. It leverages pre-trained transformer models (e.g., DistilBERT) to analyze text and categorize it into predefined sentiment classes such as “Positive,” “Neutral,” and “Negative.” This package is highly customizable and supports fine-tuning for domain-specific datasets.

You can use this tool to:

• Train sentiment analysis models on your own dataset.
• Predict the sentiment of text data in real-time or batch mode.
• Visualize training metrics such as loss curves and confusion matrices.
• To install the package and run sentiment analysis, you can clone the repository and install the dependencies:

  git clone https://github.com/LINGYUAN1201/Sentiment_Analysis.git
  cd Sentiment_Analysis
  pip install -r requirements.txt
  

  For training a new model, run:

  python train_and_test.py
  

  To use the trained model for prediction:

  python predict_sentiment.py
  

More information can be found in here.

Contributing

Contributions are welcome! If you have suggestions or find bugs, please open an issue or submit a pull request.

License

These projects are licensed under the MIT License. See the LICENSE file for details.

Contact

Author: Ling Yuan

Email: LingYUAN1201@outlook.com