topic-modeling

What problem does it solve?

Enables researchers to choose appropriate topic modeling approaches for text data, guiding interpretation and ensuring reproducible, covariate-aware analysis.

Core Features & Use Cases

• Model selection guidance: Recommends STM, LDA, or BERTopic based on data structure and covariates, with guidance on when to use each.
• Preprocessing recommendations: Clear preprocessing steps with justification (lowercasing, stopword handling, stemming, frequency thresholds).
• Model specification & diagnostics: Guidance on prevalence and content formulas, initialization, seed setting, and topic evaluation metrics like coherence and exclusivity.
• End-to-end workflow: From data prep to interpretation and reporting, with emphasis on replicability and transparency.

Quick Start

Provide a topic-modeling plan for a 2,000-document survey corpus with covariates to identify main themes and their relation to treatment groups.