An AI-driven system that transforms large-scale unstructured customer reviews into structured complaint intelligence, recurring issue detection, and automated product risk monitoring signals.
The Customer Voice Intelligence Platform (CVIP) is an end-to-end NLP system designed to:
- Detect dissatisfied customers
- Discover recurring complaint themes
- Quantify business impact of complaint categories
- Monitor issue trends over time
- Automatically flag potential product risk spikes
This project goes beyond traditional sentiment analysis and builds a structured Product Quality Intelligence System.
Organizations receive thousands of product reviews containing valuable but unstructured feedback.
Challenges: - Reviews are unstructured text - Negative reviews are rare (~4%) but high-impact - Root causes are hidden inside noisy language - Manual analysis does not scale - Issues are often detected too late
Goal: Convert raw customer voice into actionable product risk intelligence.
- ~65,000 total product reviews
- ~2,510 negative reviews (≈4%)
- Time span: 2009 -- 2018 (55 unique months)
- Binary classification (Positive vs Negative)
- TF-IDF vectorization (10,000 features)
- Logistic Regression with class imbalance handling
- Threshold tuning for minority recall optimization
Purpose: Filter dissatisfied customers reliably.
- SentenceTransformer (
all-MiniLM-L6-v2) - 384-dimensional dense semantic vectors
- Cosine similarity reasoning
- Vector-based semantic retrieval
Purpose: Move from word-frequency to meaning-based understanding.
- Density-based clustering (HDBSCAN)
- Clustering applied only to negative reviews
- Automatic detection of recurring complaint themes
Result:
- Major complaint cluster identified
- 672 reviews (26.77% of negative reviews)
- Theme: Battery & Power Reliability Issues
Common issues discovered: - Rapid battery drain - Charging failures - Premature device malfunction - High return tendency
This transforms:
Sentiment → Root Cause Intelligence
- Monthly aggregation (55 months)
- Cluster share tracking over time
- Denominator-aware validation (volume filtering)
- Stable complaint band identified (20--35%)
Insight:
Battery-related complaints consistently represented approximately 20--35% of negative reviews during peak review years (2015--2017).
Implemented:
- Month-over-month complaint share delta
- Statistical threshold detection (>10% increase)
- Minimum volume constraint (≥30 negative reviews per month)
Valid Risk Alert Months Identified:
- 2016-04
- 2016-10
- 2017-07
The system can automatically flag rising product quality risk signals.
- 26.77% of all negative reviews relate to battery reliability issues
- Complaint share stable between 20--35% during high-volume years
- Automated anomaly detection identifies meaningful spikes
- Low-volume statistical noise successfully filtered
- Python
- Pandas
- Scikit-learn
- Sentence-Transformers
- HDBSCAN
- Matplotlib
Most NLP projects: - Stop at sentiment classification.
This project: - Detects root causes. - Quantifies complaint categories. - Adds time-series monitoring. - Implements automated risk detection logic. - Integrates statistical safeguards.
It is a structured customer intelligence system.
The system enables:
- Early product defect detection
- Complaint spike monitoring
- Data-driven product improvement
- Reduced investigation time
- Proactive quality risk management
It converts:
Unstructured reviews → Structured product risk signals.
- Automated cluster labeling using LLM
- Real-time ingestion pipeline
- Dashboard integration (Streamlit / BI tool)
- Cross-metric correlation (returns + sales + complaints)
- Risk scoring model
Tej Pratap
AI & Data Intelligence Enthusiast
Focused on building applied AI systems that bridge machine learning and
business decision-making.