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topic-modeling

Select and document STM, LDA, or BERTopic topic-modeling approaches with preprocessing and diagnostics.

39|1|Updated Jan 21, 2026
Search Tags:#preprocessing#text-analysis#topic-modeling#stm#bertopic#lda#covariates
One-click install
npx skills add https://github.com/scdenney/open-science-skills --skill topic-modeling
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: topic-modeling
Source: https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/topic-modeling
Command: npx skills add https://github.com/scdenney/open-science-skills --skill topic-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about topic-modeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I choose between STM, LDA, and BERTopic for text analysis?▼

Preprocess text data for topic modeling by applying lowercasing, stopword removal, stemming, and frequency thresholds. These steps reduce noise and ensure interpretable topics before model initialization.

How do I assess covariate effects on topic prevalence in survey data?▼

Ensure topic modeling reproducibility by specifying initialization parameters, setting random seeds, and calculating required metrics like coherence and exclusivity. Maintain a clear reporting structure to document preprocessing steps and model diagnostics transparently.

What is the best way to identify interpretable themes in a large text corpus?▼

Select appropriate topic modeling approaches for text corpora with covariates by evaluating whether STM, LDA, or BERTopic aligns with your experimental data structure and research questions regarding topic prevalence.

How do I preprocess text data for topic modeling?▼

Preprocess text data for topic modeling by applying lowercasing, stopword removal, stemming, and frequency thresholds. These steps reduce noise and ensure interpretable topics before model initialization.

How do I assess covariate effects on topic prevalence in survey data?▼

Assess covariate effects on topic prevalence in survey data by using Structural Topic Models (STM). Specify prevalence formulas mapping treatment groups to topics, set initialization seeds, and evaluate results using coherence and exclusivity metrics.

How do I ensure reproducibility when running topic models?▼

Ensure topic modeling reproducibility by specifying initialization parameters, setting random seeds, and calculating required metrics like coherence and exclusivity. Maintain a clear reporting structure to document preprocessing steps and model diagnostics transparently.

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