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Feedback Topic Modeler & Summarizer
ML & AINLP pipeline that automatically categorizes and summarizes customer feedback using topic modeling.
Overview
💡 Challenge
Companies receive thousands of customer feedback messages monthly, making manual categorization and theme identification impossible.
⚡ Solution
Implemented LDA topic modeling combined with zero-shot classification to automatically group feedback into themes and generate executive summaries.
🎯 Impact
Reduced feedback analysis time from weeks to hours, surfacing actionable insights for product and support teams.
Technical Details
🛠️ Tech Stack
PythonScikit-learnNLTKHugging Face TransformersStreamlit
✨ Key Features
- ✓Automated topic extraction using LDA
- ✓Sentiment analysis per topic
- ✓Executive summary generation
- ✓Trend analysis over time
Key Learnings
- →Topic modeling requires extensive text preprocessing
- →Combining unsupervised and supervised NLP improves results
- →Domain-specific stopword lists are essential
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.