Interest Miner
Academic Research & Topic Correlation Analytics
Client Context & Objectives
An academic analytics initiative designed to model research interest trajectories of academic scholars using public publications and social discourse.
The Core Technical Challenge
Researchers lacked an automated way to combine formal scholarly publications with informal social discourse to detect emerging topic shifts and academic collaboration opportunities.
My Role & Architectural Approach
Role: Full-Stack Data Engineer. Designed NLP extraction pipelines, database models, and interactive visualization dashboards.
Built automated connectors to Twitter API and academic paper repositories. Designed keyword extraction and topic correlation algorithms to track short-term versus long-term interest shifts, visualizing results with 8+ interactive charts.
Verified Results & Business Impact
- Engineered full data pipeline from API extraction to interactive visualization.
- Formulated correlation metrics to compare research interests between scholars.
- Built 8+ custom interactive visualization charts.
Engineering Highlights
- API integration with Twitter and Google Scholar for historical data mining
- Keyword extraction and NLP segmentation to distinguish short-term vs long-term interests
- Inter-user correlation metrics to identify common research alignments
- 8+ interactive data visualization charts built with React
System Interface & Workflow Gallery
Click any screenshot to expand.
