Sentiment Analysis API using NLTK's VADER

Overview

This project is a Flask API that performs sentiment analysis using NLTK's VADER (Valence Aware Dictionary and sEntiment Reasoner). It determines whether a given text is positive, negative, or neutral based on predefined lexicon-based sentiment scores.

Features

• Rule-based sentiment analysis using NLTK's VADER.
• REST API built with Flask for easy integration.
• JSON-based input and output for seamless communication.
• Bonus: Sentiment trend visualization using Matplotlib or Seaborn.

Technologies Used

• Python
• Flask (for API development)
• NLTK (for sentiment analysis)
• Matplotlib/Seaborn (optional, for visualization)

Installation

Prerequisites

Ensure you have Python 3.7+ installed.

1. Clone the Repository

git clone https://github.com/fatma20010/sentiment-analysis-tool-using-NLTK-s-VADER.git
cd sentiment-analysis-tool-using-NLTK-s-VADER

2. Create a Virtual Environment

conda create -n sentiment_analysis
conda activate sentiment_analysis

3. Install Dependencies

4. Run the Flask API

python app.py

By default, the API runs on http://127.0.0.1:5000.

API Usage

Endpoint: /analyze

• Method: POST
• Request Format: JSON
• Request Body:

{
  "text": "I love this product!"
}

• Response: JSON with sentiment scores

{
  "neg": 0.0,
  "neu": 0.254,
  "pos": 0.746,
  "compound": 0.8316
}

Example cURL Request

curl -X POST "http://127.0.0.1:5000/analyze" -H "Content-Type: application/json" -d '{"text": "I love this product!"}'

Optional: Sentiment Trend Visualization

You can extend this project by analyzing multiple texts and visualizing sentiment trends using Matplotlib or Seaborn.

Future Improvements

• Add support for multilingual sentiment analysis.
• Implement deep learning models (BERT, LSTMs) for better accuracy.
• Deploy the API on Heroku, AWS, or Render for public use.

Contributing

Feel free to fork this repository and submit pull requests. Any improvements or bug fixes are welcome!