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Sentiment Analysis API

This project implements a backend service that exposes a RESTful API endpoint for sentiment analysis. The API accepts text input and returns the sentiment analysis result using a pre-trained machine-learning model.

Requirements

Python 3.6 or higher

Flask web framework

NLTK library

Setup

pip install -r requirements.txt

Run by command line

python app.py

or run the app.py file through any editor.

then, Send a POST request to http://localhost:5000/analyze with the following JSON payload:

{
  "text": "Text to be analyzed"
}

Ensure that the "text" field contains the text you want to analyze.

The server will return a JSON response with the sentiment analysis result:

{
  "sentiment": "positive/negative/neutral"
}

Customization You can customize the sentiment analysis logic by modifying the analyze_sentiment_text function in app.py. Replace the code inside this function to use your preferred sentiment analysis method or machine learning model.

Error Handling The API handles errors gracefully and provides appropriate error responses for invalid requests or server errors. If the payload is missing the "text" field or if there is an internal server error, the API will respond with an error message in JSON format.

Additional Notes This project uses the NLTK library for sentiment analysis. The API is implemented using the Flask web framework. You can extend the functionality or add additional endpoints as needed. Make sure to consider security measures when deploying this API to a production environment, such as enabling authentication, rate limiting, and input validation.