ReviewSense Analytics

Production-ready multilingual sentiment intelligence platform

Hybrid Transformer Routing • Confidence-Aware Decisions • Explainable AI

⭐ If you like this project, consider giving it a star!

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🎬 Live Demo

_{Real-time multilingual sentiment analysis • Bulk CSV processing • Explainability • Interactive dashboard}

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🌍 Why This Matters

• Businesses receive multilingual, mixed-language feedback daily
• Traditional models fail on code-switched (Hinglish) inputs
• Translation errors silently degrade prediction quality
• Incorrect sentiment → wrong business decisions

ReviewSense solves this with reliable, explainable, multilingual intelligence

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⚡ Key Highlights

• Hybrid transformer routing (RoBERTa + XLM-R + NLLB)
• Hinglish normalization for real-world inputs
• Translation trust validation (fail-safe fallback)
• Margin-based decision layer (ambiguity control)
• Entropy-based confidence calibration
• Explainability via LIME + ABSA
• Real-time + bulk processing pipeline

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🧩 Use Cases

• E-commerce product review analysis
• Social media sentiment monitoring
• Multilingual customer feedback systems
• Market research & brand intelligence

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🎯 Problem

• Multilingual input breaks traditional models
• Translation introduces hidden errors
• Confidence scores are misleading
• Ambiguous predictions are mishandled
• Lack of explainability

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💡 Solution Overview

Layer	Purpose
Language Routing	Detect English / Hinglish / Multilingual
Hinglish Normalization	Clean code-mixed input
Translation (NLLB)	Convert multilingual → English
Validation Layer	Verify translation quality
Model Layer	RoBERTa / XLM-R inference
Decision Layer	Margin-based ambiguity handling
Confidence Layer	Entropy calibration
Explainability	LIME + ABSA

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🧠 Core Innovations

• Model-first architecture (no heuristics)
• Margin-based ambiguity detection
• Entropy-based confidence (not softmax)
• Translation trust gating system

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🧩 Architecture Diagram

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📊 Performance

Metric	Value
Accuracy	~91%
Precision	~0.92
Recall	~0.91
F1 Score	~0.90

_{Evaluated on mixed multilingual dataset (real-world inputs)}

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🖼️ UI Preview

Home	Live Prediction
Model Dashboard	Multilingual Analysis

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🏗️ Tech Stack

Layer	Technology
Backend	FastAPI, Uvicorn
Frontend	React, TypeScript
Models	RoBERTa, XLM-R
Translation	Meta NLLB
Explainability	LIME, ABSA
ML Stack	PyTorch, Transformers
Data	Pandas, NumPy

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📂 Project Structure (Engineering-Level)

ReviewSense-Analytics/
│
├── backend/
│   ├── app/
│   │   ├── main.py
│   │   ├── routes/
│   │   ├── services/
│   │   ├── schemas/
│   │   ├── core/
│   │   └── utils/
│   └── tests/
│
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── hooks/
│   │   ├── services/
│   │   └── styles/
│
├── src/
│   ├── models/
│   ├── pipeline/
│   ├── preprocessing/
│   ├── translation/
│   ├── decision/
│   └── predict.py
│
├── docs/
│   └── images/
│
├── scripts/
├── reports/
├── data/
└── start.ps1

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🔌 API Overview

Method	Endpoint	Description
GET	/health	Health check
POST	/predict	Real-time sentiment
POST	/bulk	Bulk CSV processing
GET	/metrics	Model metrics
POST	/feedback	Feedback logging

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🛠 Setup Instructions

🔧 Prerequisites

Ensure your system has:

• Python 3.10+
• Node.js 18+
• npm or yarn
• Git

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📥 Clone Repository

git clone https://github.com/amansethhh/ReviewSense-Analytics.git
cd ReviewSense-Analytics

⚙️ Backend Setup (FastAPI)

cd backend

# Create virtual environment
python -m venv venv

# Activate environment
# Windows
venv\Scripts\activate

# macOS / Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

▶ Run Backend Server

uvicorn app.main:app --reload --port 8000

Backend running at:

http://localhost:8000

🌐 Frontend Setup (React + TypeScript)

cd frontend

npm install

▶ Run Frontend

npm run dev

Frontend running at:

http://localhost:5173

🔗 Environment Configuration

Create a .env file inside frontend/:

VITE_API_URL=http://localhost:8000

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🧪 How to Use

• 🔍 Live Prediction
  Enter multilingual or Hinglish text → get sentiment, confidence, explanation

• 📦 Bulk Analysis
  Upload CSV file → process batch sentiment predictions

• 📊 Dashboard
  View analytics, insights, and model outputs

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📁 System Flow

Input → API → NLP Pipeline → Model → Decision Layer → Output → Dashboard

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📝 Notes

First run may take time due to model loading Ensure internet connection for translation models (NLLB) Use small datasets initially for faster testing

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⚠️ Design Principles

• No heuristics
• Model-first decisions
• Deterministic outputs
• Translation-aware routing
• Fully traceable pipeline

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🔮 Future Work

• Domain-specific fine-tuning
• Advanced translation scoring
• Sarcasm detection upgrade
• CI/CD + deployment pipeline

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📜 License

MIT License

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Built with ❤️ by amansethhh