Customer Review Sentiment Intelligence Platform (NLP + Streamlit + SQL)

The 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. Designed for content, marketing, and operations teams, it provides data-driven sentiment intelligence to enhance decision-making, brand perception tracking, and product quality analysis.

Executive Summary

Organizations generate thousands of customer reviews daily, yet most remain underutilized. This solution bridges that gap, automatically classifying reviews as positive or negative, quantifying sentiment confidence, and visualizing feedback patterns over time.

Business Impact:

• Optimize product and service strategies based on real-time sentiment signals.
• Identify pain points and satisfaction drivers per product category.
• Streamline performance reporting with ready-to-present dashboards.
• Enable faster decision-making through AI-powered review analysis.

System Architecture

+------------------+
                 |  Raw Review CSV  |
                 +------------------+
                          |
                          v
                 +------------------+
                 |  Data Ingestion  |
                 | (ETL via SQLite) |
                 +------------------+
                          |
                          v
                 +------------------+
                 |  NLP Processing  |
                 |  (TF-IDF + LR)   |
                 +------------------+
                          |
                          v
                +---------------------+
                |    Model Storage    |
                | (Joblib Artifacts)  |
                +---------------------+
                          |
                          v
                +---------------------+
                |    Streamlit UI     |
                | Real-time Analytics |
                +---------------------+

Repository Structure

customer-sentiment-intelligence/
├── app/
│   └── streamlit_app.py
├── data/
│   └── reviews.db
├── models/
│   ├── model.joblib
│   └── vectorizer.joblib
├── src/
│   ├── etl_loader.py
│   ├── preprocess.py
│   └── train_model.py
└── requirements.txt

Core Capabilities

• Automated Sentiment Detection | Real-time text classification using TF-IDF + Logistic Regression.
• Interactive Review Exploration | Filter and visualize feedback by product, time, or rating.
• Confidence-Based Scoring | Probability-weighted results for transparent interpretation.
• Integrated SQL Backend | All processed reviews are persisted in SQLite for auditability.
• Scalable Architecture | Modular design ready for deployment to cloud or Docker environments.

Technical Overview

| Layer | Description | |-------|--------------| | Data Source | CSV or API-based customer reviews | | ETL Process | Data normalization, cleaning, and SQL ingestion | | Feature Engineering | TF-IDF vectorization | | Modeling | Logistic Regression (binary sentiment) | | Visualization | Streamlit UI + Plotly charts | | Persistence | SQLite database with labeled review storage |

Visual Overview

User Interface

Review Analytics Explorer

Sentiment Probability Distribution

Review Results Table

Deployment Guide

Local Setup

git clone https://github.com/yourusername/customer-sentiment-intelligence.git
cd customer-sentiment-intelligence
python -m venv venv venv\Scripts\activate  # (Windows) source venv/bin/activate  # (macOS/Linux)
pip install -r requirements.txt
streamlit run app/streamlit_app.py

Cloud Deployment (Optional)

• Package with Docker and deploy via Streamlit Cloud, Render, or Azure Web Apps.
• For enterprise environments, integrate SQLite → PostgreSQL → Power BI pipeline for advanced analytics.

Data Flow Summary

• Ingest Data: Upload or connect to raw review sources (CSV or API).
• Clean Text: Tokenization, stopword removal, lemmatization.
• Model Application: TF-IDF transforms text; logistic regression predicts sentiment.
• SQL Storage: Save predictions for traceability.
• Visualization: Streamlit renders metrics, histograms, and review tables.

Example Insights

• 67% of reviews show positive sentiment, clustered at 0.8+ probability.
• 33% are negative, primarily related to logistics and product usability.
• High-confidence classifications indicate strong model performance.
• Balanced feedback supports credible brand engagement insights.

Governance & Compliance

• Follows PEP8 coding standards.
• Model artifacts tracked via reproducible pipelines.
• SQLite ensures full audit trail for all predictions.
• Easily extendable to comply with GDPR or internal data retention policies.

Future Roadmap

• Introduce Neutral sentiment classification.
• Add Aspect-level sentiment (e.g., “delivery speed”, “customer service”).
• Enable real-time feedback API integration for live review analysis.
• Extend with topic clustering and keyword extraction.