Sentiment Dashboard

A web-based application for analyzing the sentiment of product reviews using advanced NLP models.

Description

This project provides a comprehensive solution for sentiment analysis, combining a FastAPI backend with a React frontend. The application allows users to upload CSV files containing product reviews, analyze the sentiment using a pre-trained DistilBERT model, and visualize the results through an interactive dashboard. The application consists of two main components:

1. Backend API: Built using FastAPI, providing endpoints for sentiment analysis of text data.
2. Frontend Dashboard: A user-friendly interface (built with React or similar framework) that visualizes the sentiment analysis results.

Features

• Sentiment Analysis API:

  • Analyze text data from CSV files.
  • Supports batch processing of reviews.
  • Provides detailed sentiment results including confidence scores.
  • Returns summary statistics and visualizable data.

• Dashboard Features:

  • File upload functionality for CSV files.
  • Visual representations of sentiment data through charts.
  • Tabular view of processed reviews with sentiment labels.
  • Summary statistics dashboard.

Installation

Backend Setup

1. Clone the repository:

   git clone https://github.com/Abhay-Kanwasi/Sentiment-Dashboard.git
   
   cd Sentiment Dashboard/backend

2. Create and activate a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   # OR
   .\venv\Scripts\activate  # On Windows

3. Install dependencies:

   pip install -r requirements.txt

Frontend Setup

1. Navigate to the frontend directory:

   cd Sentiment Dashboard/frontend

2. Install dependencies:

   npm install

Usage

Running the Backend

1. Start the FastAPI server:

   uvicorn app.main:app --reload

   The API will be available at http://localhost:8000.

Running the Frontend

1. Start the frontend development server:

   npm run dev

   The dashboard will be available at http://localhost:3000.

API Documentation

/analyze Endpoint

• Method: POST
• Description: Analyzes the sentiment of product reviews provided in a CSV file.
• Request Body:
  • file: CSV file containing a 'review' column with text data.
• Response:

  {
    "summary": {
      "positive_count": int,
      "negative_count": int,
      "positive_avg_confidence": float,
      "negative_avg_confidence": float,
      "total_reviews": int
    },
    "reviews": [
      {
        "review": str,
        "sentiment": str,
        "confidence": float
      }
    ]
  }

Contributing

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes with clear commit messages.
4. Push to the branch.
5. Open a Pull Request against the main branch.

Future Improvements

• Add more detailed error handling and validation.
• Implement user authentication for access control.
• Add support for different types of input files and data sources.
• Enhance the dashboard with additional visualizations and interactive features.