Case Study — AI

AI-Driven Customer Sentiment Analyzer

A social media and feedback monitoring tool performing real-time sentiment analysis, categorizing feedback into positive/negative alerts.

Project Overview

This SaaS tool tracks brand mentions across social media networks, customer reviews, and forums. It evaluates customer moods using natural language processing (NLP) pipelines, displaying live feedback streams with color-coded sentiment tags.

Key Features & Scope

Live sentiment feed updating via WebSockets (Socket.IO)

Keyword extraction highlighting trending problems or features

Automated support ticket generation for highly negative feedback

Multi-language support for international customer bases

System Architecture

React client connected to a NestJS gateway. Sentiment classification runs on a microservice wrapping Hugging Face Transformers models.

Client InterfaceNext.js / React
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Backend CoreNestJS API
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Database NodePostgreSQL & Redis
Database: PostgreSQL & Redis
Deployment: DigitalOcean Kubernetes
96
Performance
95
Accessibility
98
Best Practices
100
SEO
Verified Production Metrics
ReactNestJSHugging Face TransformersPostgreSQLSocket.IOTypeScript

Keywords and concepts covered in this project case study:

AI Sentiment AnalyzerHugging Face NLP ReactNestJS sentiment classificationSocket.IO customer service feedback

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