Sentiment Analysis Application

This project implements a Sentiment Analysis application with a React frontend, FastAPI backend, and a dedicated analysis section for exploring different machine learning models. The application allows users to input text, analyze its sentiment, and display the results.

Project Structure

• /frontend: React + TypeScript frontend.
• /backend: FastAPI backend powered by Unicorn for running sentiment analysis.
• /analysis: Model analysis and experimentation folder, dedicated to working with various sentiment analysis models.

Sentiment Analysis Overview

Sentiment analysis is the process of determining the sentiment expressed in a piece of text. It typically classifies text as positive, negative, or neutral. This project utilizes machine learning models to analyze text and classify its sentiment.

Key Features:

• Text Input: Enter text into the frontend interface.
• Sentiment Analysis: Sentiment is processed and analyzed by a FastAPI-based backend.
• Results Display: The sentiment score and classification are displayed on the frontend.
• Model Analysis: Different models for sentiment analysis can be explored and compared in the /analysis directory.

Getting Started

Prerequisites

Make sure you have the following installed:

• Node.js
• Yarn
• Python 3.x
• pip

Setup

1. Backend Setup

1. Navigate to the /backend directory:

   cd backend

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the FastAPI server with Unicorn:

   uvicorn main:app --reload

4. The backend will be accessible at http://localhost:8000.

2. Frontend Setup

1. Navigate to the /frontend directory:

   cd frontend

2. Install the required dependencies:

   yarn install

3. Start the React development server:

   yarn start

4. The frontend will be accessible at http://localhost:3000.

3. Analysis Setup

The /analysis folder contains various models and experiments for sentiment analysis. This folder is intended for researchers and data scientists looking to evaluate and compare different models.

1. Navigate to the /analysis directory:

   cd analysis

2. You can use Jupyter notebooks, scripts, or other tools to analyze and test different sentiment analysis models.