SentimentGPT

SentimentGPT is a cutting-edge project that leverages advanced Generative Pretrained Transformer (GPT) models for sentiment analysis. The project explores innovative methods such as prompt engineering, fine-tuning, and embedding-based classification to evaluate and understand sentiments in textual data.

Table of Contents

• Features
• Installation
• Usage
• Project Structure
• Contributing
• License
• Contact

Features

• Advanced Sentiment Analysis: Utilizes state-of-the-art GPT models to determine the sentiment of texts.
• Prompt Engineering: Employs prompt-based techniques to guide the GPT model for accurate sentiment detection.
• Fine-Tuning Capabilities: Offers scripts to fine-tune GPT models on custom datasets for improved performance.
• Embedding Classification: Integrates text embedding techniques for enhanced sentiment classification.

Installation

To set up the project locally, follow these steps:

1. Clone the Repository

   git clone https://github.com/swaroopms658/SentimentGPT.git
   cd SentimentGPT

2. Create a Virtual Environment (optional but recommended)

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

3. Install Dependencies

   pip install -r requirements.txt

Usage

Once the installation is complete, you can use the provided scripts to perform sentiment analysis. Below are some common tasks:

Prompt Engineering

Run the prompt engineering script with your input data:

python prompt_engineering.py --input data/your_dataset.csv --output results/predictions.csv

Fine-Tuning

Fine-tune the GPT model on your dataset:

python fine_tuning.py --dataset data/your_dataset.csv --model_output models/fine_tuned_model.pt

Embedding Classification

Classify sentiment based on text embeddings:

python embedding_classification.py --input data/your_dataset.csv --output results/embedding_predictions.csv

Project Structure

SentimentGPT/
├── data/                   # Directory for input datasets
├── models/                 # Directory to store trained models
├── results/                # Directory for output results
├── prompt_engineering.py   # Script for prompt-based sentiment analysis
├── fine_tuning.py          # Script for fine-tuning GPT models
├── embedding_classification.py  # Script for embedding-based classification
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation

Contributing

Contributions are welcome! If you’d like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes with clear commit messages.
4. Open a pull request detailing your changes.

For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License.

Contact

For any questions or further information, please reach out at:

• Email: click.com
• GitHub: swaroopms658

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Happy Sentiment Analysis!