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Customer Review Sentiment Intelligence Platform (NLP + Streamlit + SQL)\n\nThe **Customer Review Sentiment Intelligence Platform** is a production-ready analytics application that combines **Natural Language Processing (NLP)**, **SQL**, and **interactive dashboards** to deliver actionable insights on customer feedback trends.  \nDesigned for content, marketing, and operations teams, it provides **data-driven sentiment intelligence** to enhance decision-making, brand perception tracking, and product quality analysis.\n\n---\n\n## Executive Summary\n\nOrganizations generate thousands of customer reviews daily, yet most remain underutilized.  \nThis solution bridges that gap, automatically classifying reviews as *positive* or *negative*, quantifying sentiment confidence, and visualizing feedback patterns over time.\n\n**Business Impact:**\n- Optimize product and service strategies based on real-time sentiment signals.\n- Identify pain points and satisfaction drivers per product category.\n- Streamline performance reporting with ready-to-present dashboards.\n- Enable faster decision-making through AI-powered review analysis.\n\n---\n\n## System Architecture\n\n```text\n                 +------------------+\n                 |  Raw Review CSV  |\n                 +------------------+\n                          |\n                          v\n                 +------------------+\n                 |  Data Ingestion  |\n                 | (ETL via SQLite) |\n                 +------------------+\n                          |\n                          v\n                 +------------------+\n                 |  NLP Processing  |\n                 |  (TF-IDF + LR)   |\n                 +------------------+\n                          |\n                          v\n                +---------------------+\n                |    Model Storage    |\n                | (Joblib Artifacts)  |\n                +---------------------+\n                          |\n                          v\n                +---------------------+\n                |    Streamlit UI     |\n                | Real-time Analytics |\n                +---------------------+\n```\n\n---\n\n## Repository Structure\n\n```\ncustomer-sentiment-intelligence/\n├── app/\n│   └── streamlit_app.py\n├── data/\n│   └── reviews.db\n├── models/\n│   ├── model.joblib\n│   └── vectorizer.joblib\n├── src/\n│   ├── etl_loader.py\n│   ├── preprocess.py\n│   └── train_model.py\n└── requirements.txt\n```\n\n---\n\n## Core Capabilities\n\n- **Automated Sentiment Detection** | Real-time text classification using TF-IDF + Logistic Regression.  \n- **Interactive Review Exploration** | Filter and visualize feedback by product, time, or rating.  \n- **Confidence-Based Scoring** | Probability-weighted results for transparent interpretation.  \n- **Integrated SQL Backend** | All processed reviews are persisted in SQLite for auditability.  \n- **Scalable Architecture** | Modular design ready for deployment to cloud or Docker environments.  \n\n---\n\n## Technical Overview\n\n| Layer | Description |\n|-------|--------------|\n| **Data Source** | CSV or API-based customer reviews |\n| **ETL Process** | Data normalization, cleaning, and SQL ingestion |\n| **Feature Engineering** | TF-IDF vectorization |\n| **Modeling** | Logistic Regression (binary sentiment) |\n| **Visualization** | Streamlit UI + Plotly charts |\n| **Persistence** | SQLite database with labeled review storage |\n\n---\n\n## Visual Overview\n\n### User Interface  \n\u003cimg width=\"1114\" height=\"334\" alt=\"Screenshot 2025-10-28 at 12-43-23 Review Sentiment Analyzer\" src=\"https://github.com/user-attachments/assets/6df47ad3-70e4-44f1-8b80-f723021d7457\" /\u003e\n\n---\n\n### Review Analytics Explorer  \n\u003cimg width=\"1088\" height=\"553\" alt=\"Screenshot 2025-10-28 at 12-43-43 Review Sentiment Analyzer\" src=\"https://github.com/user-attachments/assets/03b6e7cb-269c-4d57-95af-71745a17595e\" /\u003e\n\n---\n\n### Sentiment Probability Distribution  \n\u003cimg width=\"1028\" height=\"395\" alt=\"Screenshot 2025-10-28 at 12-43-53 Review Sentiment Analyzer\" src=\"https://github.com/user-attachments/assets/922b886a-ae02-4192-a383-81a5ebafa038\" /\u003e\n\n---\n\n### Review Results Table  \n\u003cimg width=\"1039\" height=\"465\" alt=\"Screenshot 2025-10-28 at 12-44-08 Review Sentiment Analyzer\" src=\"https://github.com/user-attachments/assets/ef21d1de-476c-4550-9fd9-3718deadb196\" /\u003e\n\n---\n\n## Deployment Guide\n\n### Local Setup\n```bash\ngit clone https://github.com/yourusername/customer-sentiment-intelligence.git\ncd customer-sentiment-intelligence\n\npython -m venv venv\nvenv\\Scripts\\activate  # (Windows)\nsource venv/bin/activate  # (macOS/Linux)\n\npip install -r requirements.txt\n\nstreamlit run app/streamlit_app.py\n```\n\n### Cloud Deployment (Optional)\n- Package with **Docker** and deploy via **Streamlit Cloud**, **Render**, or **Azure Web Apps**.  \n- For enterprise environments, integrate SQLite → PostgreSQL → Power BI pipeline for advanced analytics.\n\n---\n\n## Data Flow Summary\n\n1. **Ingest Data:** Upload or connect to raw review sources (CSV or API).  \n2. **Clean Text:** Tokenization, stopword removal, lemmatization.  \n3. **Model Application:** TF-IDF transforms text; logistic regression predicts sentiment.  \n4. **SQL Storage:** Save predictions for traceability.  \n5. **Visualization:** Streamlit renders metrics, histograms, and review tables.\n\n---\n\n## Example Insights\n\n- 67% of reviews show **positive sentiment**, clustered at **0.8+ probability**.  \n- 33% are **negative**, primarily related to logistics and product usability.  \n- High-confidence classifications indicate strong model performance.  \n- Balanced feedback supports credible brand engagement insights.\n\n---\n\n## Governance \u0026 Compliance\n\n- Follows **PEP8** coding standards.  \n- Model artifacts tracked via reproducible pipelines.  \n- SQLite ensures full audit trail for all predictions.  \n- Easily extendable to comply with **GDPR** or internal data retention policies.\n\n---\n\n## Future Roadmap\n\n- Introduce **Neutral sentiment** classification.  \n- Add **Aspect-level sentiment** (e.g., “delivery speed”, “customer service”).  \n- Enable **real-time feedback API** integration for live review analysis.  \n- Extend with **topic clustering** and **keyword extraction**.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirhosseinhonardoust%2Fcustomer-sentiment-intelligence-platform","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famirhosseinhonardoust%2Fcustomer-sentiment-intelligence-platform","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirhosseinhonardoust%2Fcustomer-sentiment-intelligence-platform/lists"}