{"id":28277211,"url":"https://github.com/rushilpatel21/sentique","last_synced_at":"2026-02-24T00:48:38.282Z","repository":{"id":288592545,"uuid":"936976269","full_name":"rushilpatel21/Sentique","owner":"rushilpatel21","description":"Sentique is a full‑stack feedback analytics platform that ingests user reviews from App Store, Google Play, Trustpilot, Reddit and Twitter/X, processes them with a fine‑tuned BERT model into 16 categories and sentiment labels, and provides comprehensive analytics and insights.","archived":false,"fork":false,"pushed_at":"2025-11-02T09:50:49.000Z","size":87754,"stargazers_count":2,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-02T11:31:11.935Z","etag":null,"topics":["bert-model","celery","chatbot","data-visualization","deep-learning","django","feedback-analysis","finetuned-model","gemini-api","machine-learning","nlp","postgresql","rag","rag-chatbot","react","review-analysis","scraping","sentiment-analysis","sentique","vector-search"],"latest_commit_sha":null,"homepage":"https://sentique.vercel.app","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rushilpatel21.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-22T03:36:26.000Z","updated_at":"2025-11-02T09:50:53.000Z","dependencies_parsed_at":"2025-10-02T13:14:10.067Z","dependency_job_id":null,"html_url":"https://github.com/rushilpatel21/Sentique","commit_stats":null,"previous_names":["rushilpatel21/sentique"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rushilpatel21/Sentique","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilpatel21%2FSentique","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilpatel21%2FSentique/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilpatel21%2FSentique/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilpatel21%2FSentique/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rushilpatel21","download_url":"https://codeload.github.com/rushilpatel21/Sentique/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilpatel21%2FSentique/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29764260,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-23T21:02:23.375Z","status":"ssl_error","status_checked_at":"2026-02-23T20:58:31.539Z","response_time":90,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bert-model","celery","chatbot","data-visualization","deep-learning","django","feedback-analysis","finetuned-model","gemini-api","machine-learning","nlp","postgresql","rag","rag-chatbot","react","review-analysis","scraping","sentiment-analysis","sentique","vector-search"],"created_at":"2025-05-21T06:15:10.649Z","updated_at":"2026-02-24T00:48:38.273Z","avatar_url":"https://github.com/rushilpatel21.png","language":"TypeScript","readme":"# Sentique\n\n**Sentique** is a full‑stack feedback analytics platform that ingests user reviews from App Store, Google Play, Trustpilot, Reddit and Twitter/X, processes them with a fine‑tuned BERT model into 16 categories and sentiment labels, and provides comprehensive analytics and insights.\n\n## Overview\n\nSentique transforms scattered customer feedback into actionable intelligence through sophisticated data processing and AI-powered analysis. The platform combines traditional machine learning with generative AI to deliver both structured analytics and conversational insights, helping businesses understand customer sentiment at scale.\n\n## Screenshots\n\n### Onboarding Form  \n![Onboarding Form](sentique_screenshots/Sentique-Screenshots/Onboarding.png)  \n*The onboarding form collects essential application details via the `/register` endpoint to set up Sentique:*  \n- Apple App Store Product ID (numeric ID, e.g. `123456789`)  \n- Apple App Store Product Name (e.g. `MyAwesomeApp`)  \n- Google Play Store Package Name (e.g. `com.example.app`)  \n- Company Website URL (e.g. `example.com`)  \n\n*Once you submit this information, Sentique will automatically fetch and process reviews from your configured app stores and web channels for sentiment analysis and insights.*\n\n### Dashboard Overview\n![Dashboard Overview](sentique_screenshots/Sentique-Screenshots/Dashboard_1.png)\n*The main dashboard provides a comprehensive view of sentiment trends across time periods, sources, and categories. Key metrics are highlighted with interactive filtering capabilities.*\n\n### Sentiment Analysis\n![Sentiment Analysis](sentique_screenshots/Sentique-Screenshots/Sentiment_1.png)\n*Detailed sentiment breakdown showing positive, negative, and neutral distribution across different sources and time periods, enabling targeted analysis of customer feedback patterns.*\n\n### Actionable Insights\n![Category Distribution](sentique_screenshots/Sentique-Screenshots/Analysis_1.png)\n*Summarization of review distribution across the 16 categories identified by our fine-tuned BERT model, helping identify the most discussed aspects of products or services.*\n\n![Category Distribution](sentique_screenshots/Sentique-Screenshots/Analysis_2.png)\n*Actionable Insights of review distribution across the 16 categories identified by our fine-tuned BERT model, helping identify the most discussed aspects of products or services.*\n\n### RAG-Based Chatbot Interface \n![RAG Chatbot](sentique_screenshots/Sentique-Screenshots/RAG_1.png)\n*Natural language interface allowing users to query the review database conversationally, with responses generated by Gemini API using relevant review context.*\n\n### Insights Report\n![Insights Report](sentique_screenshots/Sentique-Screenshots/Report_1.png)\n*Summary report highlighting key strengths and improvement areas based on analysis of customer reviews across selected time periods and categories.*\n\n## Key Features\n\n- **Multi-Source Data Collection**: Automated scraping of reviews from App Store, Google Play, Trustpilot, Reddit, and Twitter/X\n- **ML-Powered Classification**: Fine-tuned BERT model categorizes reviews into 16 distinct classes\n- **Sentiment Analysis**: Identifies positive, negative, and neutral opinions across all feedback sources\n- **Interactive Dashboards**: Real-time visualization of sentiment trends, source breakdowns, and feature-specific feedback\n- **AI-Powered Insights**: Actionable summaries of what users like and areas for improvement via Gemini API\n- **RAG-Driven Chatbot**: Natural language Q\u0026A over vectorized review data using SentenceTransformer + Gemini\n\n## Tech Stack\n- **Frontend**: React, Vite, TypeScript, Tailwind CSS\n- **Backend**: Django, Django REST Framework, FastAPI\n- **Database**: PostgreSQL, PGVector, Redis (Celery broker)\n- **Authentication**: Django all-auth\n- **LLM**: Gemini 1.5 Pro\n- **Deep Learning Models**: Fine-tuned ROBERTA based classifier model, j-hartmann/sentiment-roberta-large-english-3-classes\n- **Network Tunneling**: ngrok\n- **Process Management**: Celery\n- **Package Management**: pnpm (for frontend), pip (for backend)\n\n## System Architecture\n\n### Backend Architecture\n\n#### Data Collection Framework\n- **Multi-Source Scrapers**: Specialized modules for each data source (App Store, Play Store, Reddit, Trustpilot, Twitter)\n- **Asynchronous Processing**: Django + Celery architecture for efficient task queuing and execution\n- **Error Handling**: Robust retry mechanisms for failed scraping attempts\n- **Data Storage**: PostgreSQL database optimized for review data and analysis results\n\n#### Machine Learning Pipeline\n- **Sentiment Analysis**: Classifies reviews as positive, negative, or neutral\n- **Category Classification**: Fine-tuned BERT model sorts reviews into 16 specific categories\n- **Vector Embeddings**: Transforms review text into vector representations for similarity search\n- **Model Persistence**: Efficient storage and retrieval of trained models\n\n#### AI-Powered Insights\n- **Gemini API Integration**: Generates comprehensive insights about specific feedback categories\n- **Structured Analysis**: Creates organized summaries of positive and negative aspects\n- **Time-Series Analysis**: Tracks sentiment changes and feature performance over time\n- **Fallback Handling**: Alternative text extraction when primary parsing methods fail\n\n#### RAG-Based Chatbot\n- **Vector Search**: SentenceTransformer converts queries into searchable vectors\n- **Contextual Retrieval**: Finds and ranks relevant reviews based on semantic similarity\n- **Context Augmentation**: Enhances AI responses with retrieved review data\n- **Response Generation**: Gemini API produces human-like answers grounded in actual review data\n\n### Frontend Architecture\n\n#### Component Structure\n- **React + TypeScript**: Modern frontend with strong typing\n- **Dashboard Views**: Main analysis dashboards with sentiment trends\n- **Detailed Feedback Explorer**: Deep-dive interface for individual reviews\n- **Product Feedback Analysis**: Category-specific review insights\n- **Interactive Chat Interface**: Conversational UI for the RAG system\n\n#### Visualization Components\n- **Trend Charts**: Time-based visualization of sentiment patterns\n- **Source Distribution**: Breakdown of sentiment by data source\n- **Feature Analysis**: Comparative visualization of product features\n- **Topic Modeling**: Visual representation of common themes in reviews\n\n## Data Flow\n\n1. **Collection**: Scrapers gather reviews from multiple platforms\n2. **Processing**: ML models analyze sentiment and categorize reviews\n3. **Enrichment**: Reviews are vectorized and stored with metadata\n4. **Analysis**: Gemini API generates insights from processed data\n5. **Presentation**: Frontend displays visualizations and interactive reports\n6. **Interaction**: Users explore data and query the RAG chatbot\n\n## Project Setup Guide\n\n### Notes\n- Requires **pnpm**, **Node.js**, **Python 3.9+**\n- On Windows, use `venv\\Scripts\\activate` instead of `source venv/bin/activate`\n- Environment variables must be configured in `.env` file\n\n### Frontend\n\n1. Navigate to the frontend directory:\n   ```sh\n   cd frontend\n   ```\n\n2. Install dependencies:\n   ```sh\n   pnpm install\n   ```\n\n3. Start the development server:\n   ```sh\n   pnpm dev\n   ```\n\n\n\n\n### Database Setup\n\n#### Install PostgreSQL\nIf you haven't installed PostgreSQL, install it using:\n\n**Ubuntu:**\n```sh\nsudo apt update\nsudo apt install postgresql postgresql-contrib\n```\n\n**Mac (Homebrew):**\n```sh\nbrew install postgresql\n```\n\n**Windows:**\nDownload from [official PostgreSQL website](https://www.postgresql.org/download/).\n\n#### Enable and Start PostgreSQL\n```sh\nsudo systemctl enable postgresql\nsudo systemctl start postgresql\n```\n\n#### Create a Database and User\n```sh\nsudo -u postgres psql\n```\nThen, inside the PostgreSQL shell:\n```sql\nCREATE DATABASE sentique;\nCREATE USER sentique_user WITH PASSWORD 'my_password';\nALTER ROLE sentique_user SET client_encoding TO 'utf8';\nALTER ROLE sentique_user SET default_transaction_isolation TO 'read committed';\nALTER ROLE sentique_user SET timezone TO 'UTC';\nGRANT ALL PRIVILEGES ON DATABASE sentique TO sentique_user;\n\\q\n```\n\n#### Install `pgvector` Extension\nConnect to your database:\n```sh\npsql -U sentique_user -d sentique\n```\nThen, enable the `pgvector` extension:\n```sql\nCREATE EXTENSION vector;\n```\n\n### Redis Setup\n\n#### Install Redis\n\n**Ubuntu:**\n```sh\nsudo apt update\nsudo apt install redis-server\n```\n\n**Mac (Homebrew):**\n```sh\nbrew install redis\n```\n\n**Windows:**\nUse [Memurai](https://www.memurai.com/) (a Redis alternative for Windows) or install Redis via WSL.\n\n#### Start and Enable Redis\n```sh\nsudo systemctl enable redis-server\nsudo systemctl start redis-server\n```\n\n#### Verify Redis is Running\n```sh\nredis-cli ping\n```\nYou should see `PONG` as output.\n\n\n### Backend\n\n1. Create and activate a virtual environment:\n   ```sh\n   python -m venv venv\n   source venv/bin/activate  # For macOS/Linux\n   # For Windows:\n   # venv\\Scripts\\activate\n   ```\n\n2. Install required dependencies:\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n3. Apply database migrations:\n   ```sh\n   python manage.py makemigrations\n   python manage.py migrate\n   ```\n\n4. Run the celery worker:\n   ```sh\n   celery -A backend worker --loglevel=info\n   ```\n\n5. Run the development server:\n   ```sh\n   python manage.py runserver\n   ```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frushilpatel21%2Fsentique","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frushilpatel21%2Fsentique","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frushilpatel21%2Fsentique/lists"}