topic-extractor

Topic discovery uses LLM semantic clustering or keyword-based grouping on community posts to identify hidden themes. It ranks topics by popularity and sentiment, extracting representative content to show exactly what users are discussing.

The best way to categorize user comments is through hierarchical topic structuring, which organizes community feedback into a logical tree. This clusters hidden themes and subjects, extracting representative content for each topic to improve comprehension.

Yes, sentiment and popularity rankings are extracted automatically from user posts. The topic extraction process clusters community feedback themes and ranks them based on overall popularity and sentiment analysis results.

Topic modeling works effectively for analyzing app reviews and feature requests. It clusters large volumes of community feedback to uncover hidden themes, identifying specific subjects like login issues or UI design feedback for quick analysis.

Keyword-based grouping for community feedback analysis may struggle with nuanced context compared to LLM semantic clustering. While it identifies explicit feature mentions, semantic clustering provides deeper topic identification and hierarchical structuring for complex discussions.