MalaikaJunaid/Sentiment-Analyzer-Backend

A web application that analyzes sentiment and emotions in text using NLP. Features user authentication, sentiment visualization, and a dashboard for tracking analysis history.

Sentiment Analyzer Backend

This project is a backend service for a sentiment analyzer web application. It includes features like emotion detection, language detection, and sentiment analysis, with user authentication and data management capabilities.

Table of Contents

• Features
• Installation
• Usage
• API Endpoints
• Technologies Used
• Contributing

Features

• User Authentication: Sign up and log in functionality to manage user sessions.
• Sentiment Analysis: Analyze text and determine the sentiment as positive or negative.
• Emotion Detection: Identify emotions from the input text.
• Language Detection: Determine the language of the input text.
• Data Visualization: View analysis results with graphical representations.

Installation

Prerequisites

• Python 3.8+
• Virtualenv or pipenv

Steps

1. Clone the Repository

   git clone https://github.com/your-username/sentiment-analyzer-backend.git
   cd sentiment-analyzer-backend
      

2. Set Up Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use          
   `venv\Scripts\activate`
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Set Up Database:

   python database.py
   

5. Run the application:

   python app.py
   

Endpoints

• POST /signup: Create a new user account.
• POST /login: Authenticate a user.
• POST /analyze: Analyze sentiment of text.
• POST /detect-emotion: Detect emotion in text.
• POST /detect-language: Detect language of text.
• GET /user/history: Retrieve the user's analysis history.

Technologies Used

• Flask: Web framework for Python.
• Dask: Parallel computing with task scheduling.
• Transformers: Pre-trained models for natural language processing.
• SQLite: Lightweight database for storing user and analysis data.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request