A dynamic review management platform integrating NLP-powered sentiment analysis, clustering, and recommendation systems to enhance user insights and product feedback.
- Sentiment Analysis
Automatically detect and classify reviews as positive or negative using a threshold-based sentiment model. - Tag Extraction
Extract the most common tags from user reviews using Named Entity Recognition (NER). - Review Clustering
Group similar reviews using K-Means clustering, enabling better insights into customer opinions. - Recommendation Engine
Suggest products to users based on their review history and preferences. - Data Visualization
- View common tags in a horizontal stack and a pie chart.
- Explore overall sentiment in bar and pie charts.
- User-Friendly Interface
- Add, view, and manage reviews through an interactive UI.
- See scrollable lists of reviews sorted by timestamps.
- Python 3.7+
- Libraries:
sqlite,uvicorn,fastapi,streamlit,spacy,scikit-learn
- FastAPI: Handles API endpoints and CRUD operations.
- SQLite: Lightweight database for persisting product, review, and user data.
- Spacy: Used for Named Entity Recognition (NER) and generating text embeddings for clustering.
- TextBlob: Provides sentiment analysis of review text.
- Scikit-Learn: Implements K-Means clustering for grouping reviews.
- Streamlit: Offers a clean, interactive UI for submitting and visualizing review data.
Review-Sphere/
│
├── backend/
│ ├── app/
│ │ ├── __init__.py
│ │ ├── main.py # FastAPI entry point
│ │ ├── database.py # Database models and operations
│ │ ├── models.py # Database schema initialization
│ │ ├── nlp_utils.py # NLP and clustering utilities
│ ├── requirements.txt # Backend dependencies
│ └── uvicorn_runner.py # Script to start FastAPI server
│
├── frontend/
│ ├── app.py # Streamlit frontend application
│ ├── requirements.txt # Frontend dependencies
│
└── store.db # SQLite database
-
Create a virtual environment
conda create --name env_name conda activate env_name
-
Navigate to the
backenddirectory:cd backend -
Install dependencies:
pip install -r requirements.txt
-
Start the FastAPI server:
uvicorn app.main:app --reload
- Navigate to the
frontenddirectory:cd frontend - Install dependencies:
pip install -r requirements.txt
- Run the Streamlit app:
streamlit run app.py
- Open your browser at the URL provided by Streamlit (default : http://localhost:8501).
- Select a product from the dropdown, submit reviews, and explore visualizations for sentiment analysis, tags, and clusters.
- View personalized recommendations for similar products.
- Deep Learning Integration: Replace traditional models with Transformer-based architectures for improved sentiment analysis and tagging.
- Collaborative Filtering: Enhance the recommendation engine with collaborative filtering for better user-product matching.
- Language Support: Add support for multiple languages in reviews.
- Customizable Dashboards: Allow users to create custom reports and dashboards.
Contributions are welcome! Please fork the repository, create a branch, and submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
Enjoy Reviewing the world of Recommendations! 🚀