Sentiment Analysis Web App

This project is a simple Sentiment Analysis Web Application that uses a FastAPI backend and an HTML, CSS, and JavaScript frontend. The backend leverages a pre-trained DistilBERT model to classify input text as positive or negative.

Features

• Analyze sentiment (Positive/Negative) of input text.
• FastAPI backend with CORS enabled.
• Frontend built with HTML, CSS, and JavaScript to interact with the API.
• Uses Hugging Face's distilbert-base-uncased-finetuned-sst-2-english model for sentiment classification.
• Interactive character in the frontend that changes based on the sentiment response.

Technologies Used

• Backend: FastAPI, Transformers (Hugging Face), PyTorch
• Frontend: HTML, CSS, JavaScript

Installation & Setup

Backend Setup

1. Clone this repository:

   git clone https://github.com/Pr0-T0/Bert_sentiment_Analysis.git
   cd Bert_sentiment_Analysis

2. Create a virtual environment (optional but recommended):

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

3. Install dependencies:

   pip install fastapi uvicorn torch transformers pydantic

4. Run the FastAPI server:

   uvicorn app:app --host 0.0.0.0 --port 8000

5. The API will be accessible at http://localhost:8000.

Frontend Setup

1.The frontend will be available at http://localhost:8000/static.

API Endpoints

• POST /sentiment: Accepts a JSON object with a text field and returns the sentiment analysis result.

  Request Example:

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

  Response Example:

  {
    "sentiment": "Positive"
  }

Folder Structure

/
├── backend/
│   ├── app.py  # FastAPI server
├── frontend/
│   ├── index.html  # Frontend UI
│   ├── styles.css  # Styling
│   ├── script.js  # Handles API calls and character animations

Future Improvements

• Improve UI/UX with a better design.
• Deploy the app online.
• Extend model capabilities for more sentiment categories.
• Enhance the character animations for more emotional expressions.