Sentiment Analysis

This is a sentiment analysis web application that utilizes a fine-tuned model to classify statements into three categories: Political, Regional, and Sports. The application is built using React for the frontend and Python/Node.js/Express.js for the backend. This application also detects postive and negative statements used to detect social media sentiment detection.

---

Features

• Classifies statements into Political, Regional, or Sports categories.
• Utilizes a fine-tuned model for accurate sentiment analysis.
• Supports database integration with tables for results and users.

---

Getting Started

Step 1: Clone the Repository

Step 2: Install Dependencies

npm install
pip install -r requirements.txt

Step 3: Train the Model

Run the training script with the provided dataset:

python train_model.py

Step 4: Set Up the Database

1. Create a database in MySQL.
2. Execute the SQL commands below to create the required tables:

CREATE TABLE users (
    id INT AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(100),
    email VARCHAR(100),
    password_hash VARCHAR(255)
);

CREATE TABLE results (
    id INT AUTO_INCREMENT PRIMARY KEY,
    user_id INT,
    input_text TEXT,
    category VARCHAR(50),
    FOREIGN KEY (user_id) REFERENCES users(id)
);

Step 5: Run the frontend and backend commands

1. Frontend

npm start

2. Start the Python backend:

python app.py

4. Start the Node.js server:

node --experimental-modules server.mjs