This repository contains the code and resources for a sentiment analysis project specifically focused on airline reviews. The goal of this project is to analyze the sentiment expressed in customer reviews of airlines and classify them as positive, negative, or neutral. The project is deployed using FastAPI, allowing for easy integration and usage.

• Preprocessing: The project includes preprocessing techniques like tokenization, removing stop words, and stemming/lemmatization to clean the text data from airline reviews.
• Sentiment Classification: The sentiment analysis model utilizes machine learning or deep learning algorithms to classify the sentiment of the airline reviews.
• FastAPI Deployment: The project is deployed using FastAPI, providing a robust and efficient web framework for serving the sentiment analysis model as an API.
• API Documentation: The project includes documentation for the API endpoints, allowing users to understand how to interact with the sentiment analysis service.

1. Clone the repository: git clone https://github.com/srivasPankhuri/Sentiment-Analysis-API.git
2. Navigate to the project directory: cd Sentiment-Analysis-API
3. Install the required dependencies: pip install -r requirements.txt

1. Prepare your airline review data by placing it in the designated folder.
2. Run the preprocessing script: Sentiment_Analysis.ipynb
3. Train the sentiment analysis model: main.py
4. Start the FastAPI server: uvicorn main:app --reload

Once the server is up and running, you can interact with the sentiment analysis API by sending HTTP requests to the provided endpoints. Refer to the API documentation for detailed information on how to make requests and interpret the responses.

Contributions are welcome! If you want to contribute to this project, please fork the repository and create a new branch. Submit any pull requests or open issues for further discussion.

We would like to thank the contributors and the open-source community for their valuable resources and inspiration used in building this sentiment analysis project. Additionally, we appreciate the FastAPI developers for providing a powerful and efficient web framework for deployment.