Sentiment Analysis Application

This project is a full-stack application that performs sentiment analysis on user-input text. It consists of a React frontend and a Python Flask backend.

Frontend

The frontend is built with React and TypeScript, styled using Tailwind CSS, and structured with modular components.

Project Structure

frontend/ ├── public/ ├── src/ │ ├── components/ │ │ ├── SentimentForm.tsx │ │ └── SentimentResult.tsx │ ├── App.tsx │ ├── index.tsx │ └── types.ts ├── package.json ├── tsconfig.json └── README.md

Key Features

• TypeScript for type safety
• Modular component structure
• Tailwind CSS for styling

Setup

• Navigate to the frontend directory:

• cd frontend

• Install the required packages: npm install

• Install TypeScript and type definitions: npm install --save-dev typescript @types/react @types/react-dom @types/node

Running the Frontend

• From the frontend directory, run: npm start The application will be available at http://localhost:3000.

Components

• App.tsx: The main component that renders the application structure and manages the overall state.
• SentimentForm.tsx: Renders the form for text input and handles the sentiment analysis request.
• SentimentResult.tsx: Displays the results of the sentiment analysis, including a visual representation of the sentiment score.

Types

• The types.ts file contains TypeScript interfaces used throughout the application, including:

export interface AnalysisResult { text: string; sentiment: number; sentiment_label: string; }

Styling

The application uses Tailwind CSS for styling. The main styles are applied in the individual component files.

Testing

To run the frontend tests: npm test

Building for Production

To create a production build: npm run build This will generate optimized static files in the build directory.

Backend

The backend is built with Python using the Flask framework and TextBlob for sentiment analysis.

Setup

1. Navigate to the backend directory:

   cd backend
   

2. Create and activate a virtual environment:

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

3. Install the required packages:

   pip install -r requirements.txt
   

Running the Backend

From the backend directory, run:

python run.py

The server will start on http://localhost:5000.

Using the Application

1. Ensure both the backend and frontend servers are running.
2. Open a web browser and go to http://localhost:3000.
3. Enter text in the provided textarea and click "Analyze".
4. The sentiment analysis results will be displayed below the input field.

API Endpoint

The backend provides a single API endpoint:

• URL: /analyze
• Method: POST
• Body: JSON object with a text field
• Response: JSON object with text, sentiment (score), and sentiment_label

Example curl request:

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

Testing

Backend Tests

From the backend directory, run:

python -m unittest test_app.py

Frontend Tests

From the frontend directory, run:

npm test

Future Improvements

• Implement user authentication - DONE
• Add support for analyzing multiple texts at once
• Integrate with social media APIs for real-time sentiment analysis
• Improve sentiment analysis accuracy with machine learning models

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.