Aspectify¶

Welcome to Aspectify - A powerful Aspect-Based Sentiment Analysis (ABSA) system powered by Google's Gemini AI.

Overview¶

Aspectify is a production-ready application that extracts aspects (features/topics) from text and analyzes the sentiment (positive/negative/neutral) associated with each aspect. It's designed for analyzing customer reviews, feedback, and opinions at a granular level.

Key Features¶

🎯 Aspect Extraction: Automatically identifies key topics and features mentioned in text
😊 Sentiment Analysis: Determines sentiment for each extracted aspect
📊 Batch Processing: Handles single texts or bulk CSV uploads efficiently
🌍 Multi-language Support: Auto-detects language (Turkish, English, etc.)
⚡ Fast & Scalable: Async processing with concurrent batch handling
🎨 Interactive Frontend: Beautiful Streamlit UI with visualizations
📈 Export Options: Download results as CSV or Excel

Use Cases¶

Product Reviews: Analyze what customers love or hate about specific product features
Service Feedback: Understand satisfaction levels across different service aspects
Brand Monitoring: Track sentiment changes for various brand attributes
Market Research: Extract insights from survey responses and feedback forms

Quick Start¶

Installation¶

# Clone repository
git clone https://github.com/hanifekaptan/ai-agent-for-aspect-based-sentiment-analysis
.git
cd ai-agent-for-aspect-based-sentiment-analysis

# Create virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1  # Windows
source .venv/bin/activate    # Linux/Mac

# Install dependencies
pip install -r requirements.txt

Running the Application¶

Backend API:

uvicorn app.main:app --reload --reload-dir app

Frontend UI:

streamlit run frontend/app.py

Example¶

Input:

The phone's screen is amazing but the battery drains too quickly.

Output:

{
  "id": "1",
  "aspects": [
    {"term": "screen", "sentiment": "positive"},
    {"term": "battery", "sentiment": "negative"}
  ]
}

Architecture¶

The system consists of three main components:

FastAPI Backend: RESTful API for analysis requests
Streamlit Frontend: Interactive web interface with visualizations
LLM Integration: Google Gemini for aspect extraction and sentiment classification

See the Architecture page for detailed system design.

API Reference¶

For detailed API endpoints and usage, see the API Reference page.

Technology Stack¶

Backend: FastAPI, Python 3.10+
LLM: Google Gemini (via LangChain)
Frontend: Streamlit, Plotly
Data Processing: Pandas, Jinja2
Testing: Pytest

License¶

This project is licensed under the Apache License.

Contributing¶

Contributions are welcome! Please feel free to submit a Pull Request.

Support¶

For issues and questions, please open an issue on GitHub.