Sentiment Analyzer Application Development by Hashir ahmedSentiment Analyzer Application Development by Hashir ahmed

Sentiment Analyzer Application Development

Hashir ahmed

AI Agent Designer

Backend Engineer

React Native Developer

Node.js

React

Supabase

E-Commerce

Sentiment Analyzer

A modern sentiment analysis application that analyzes text from various sources including direct input, YouTube, e-commerce, news, social media, and blogs.

Overview

This application uses a custom sentiment analysis model to detect positive, negative, and neutral sentiments in text content. It features a React frontend and a Supabase Edge Function backend that performs the sentiment analysis using a custom implementation.

Features

Direct Text Analysis: Analyze any text input for sentiment

URL-based Analysis: Mock integration with various platforms:

YouTube videos

E-commerce product reviews

News articles

Social media posts

Blog content

Sentiment Visualization: Clear visual representation of sentiment with confidence scores

Responsive Design: Works on desktop and mobile devices

Technology Stack

Frontend:

React with TypeScript

TailwindCSS for styling

Vite for development and building

Backend:

Supabase Edge Functions (Deno runtime)

Custom sentiment analysis model

Sentiment Analysis Model

The application uses a custom sentiment analysis model that:

Identifies positive, negative, and neutral sentiments

Handles negations (e.g., "not good" as negative)

Processes intensifiers like exclamation marks

Provides confidence scores

Returns normalized sentiment scores from -1 (negative) to +1 (positive)

Installation

Prerequisites

Node.js 16+

npm or yarn

Deno (for local Edge Function development)

Supabase CLI (for deploying Edge Functions)

Setup

Clone the repository:

git clone https://github.com/yourusername/sentiment-analyzer.git
cd sentiment-analyzer

Install dependencies:

npm install

Set up environment variables:

cp .env.example .env.local

Edit .env.local with your Supabase URL and anon key

Run development server:

npm run dev

In a separate terminal, run the Edge Function locally:

cd supabase/functions/analyze-text
deno run --allow-read --allow-net --allow-env index.ts

Usage

Direct Text Analysis

Enter text in the "Text to Analyze" field

Click "Analyze Text"

View the sentiment results, including:

Sentiment classification (positive, negative, or neutral)

Confidence score

Visualization of sentiment score on a scale

URL Analysis (Mock)

Enter a URL in the input field

Select the appropriate category (YouTube, e-commerce, etc.)

View the aggregated sentiment analysis for the content

Browse through sample comments/reviews with individual sentiment ratings

Deployment

Frontend

Deploy to Vercel, Netlify or any static hosting service:

npm run build

Then deploy the contents of the dist folder.

Edge Function

Deploy to Supabase:

cd supabase/functions/analyze-text
supabase functions deploy analyze-text

Project Structure

/
├── src/                  # Frontend source code
│   ├── components/       # React components
│   ├── types/            # TypeScript type definitions
│   ├── utils/            # Utility functions and mock APIs
│   ├── App.tsx           # Main application component
│   └── main.tsx          # Entry point
├── supabase/
│   └── functions/
│       └── analyze-text/ # Edge Function for sentiment analysis
│           ├── index.ts  # Entry point for Edge Function
│           └── model.ts  # Sentiment analysis model implementation
├── public/               # Static assets
└── index.html            # HTML template