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topic-modeling-lit

Preprocess scientific abstracts and fit LDA or BERTopic models to discover latent themes.

33|6|Updated Mar 17, 2026
Search Tags:#literature review#topic modeling#bertopic#scientific abstracts#lda coherence#dynamic trends#pyldavis
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill topic-modeling-lit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: topic-modeling-lit
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/21-library-science/topic-modeling-lit
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill topic-modeling-lit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Scientific literature topic modeling turns large abstract collections into understandable themes, helping you perform faster and more reliable literature reviews than manual reading alone.

Core Features & Use Cases

  • LDA with coherence-driven topic selection: preprocess abstracts, train LDA across multiple topic counts, and choose the best model using C_v coherence.
  • BERTopic clustering for semantic topics: use sentence embeddings plus UMAP + HDBSCAN to discover fine-grained topic clusters from abstracts.
  • Dynamic topic modeling and exportable outputs: visualize topic evolution over time (BERTopic topics-over-time), export human-readable topic summaries, and generate interactive PyLDAvis HTML for LDA results.
  • Common use case: analyze 2015–2024 abstracts from a field (e.g., “deep learning neural network”) to identify dominant themes and how they shift year over year, with publication-ready topic summaries and interactive visualizations.

Quick Start

Use the topic-modeling-lit skill to take a list of scientific abstract strings, fit an LDA model with coherence-based topic selection, and generate an interactive PyLDAvis HTML visualization for the best model.

Frequently Asked Questions about topic-modeling-lit

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

FAQPage Schema
What's the best way to discover latent themes from scientific abstracts for a literature review?▼

Scientific literature topic modeling reveals latent themes by preprocessing abstracts and fitting LDA or BERTopic models. It transforms large abstract collections into understandable themes for faster, more reliable literature reviews than manual reading.

How do I select the optimal number of topics when training an LDA model on research papers?▼

Select the optimal LDA topic count using coherence-based model selection. The process trains LDA across multiple topic numbers and evaluates C_v coherence scores to identify the best performing model for your abstract corpus.

Can I use BERTopic with sentence embeddings to find fine-grained clusters in abstracts?▼

Yes, BERTopic clusters semantic topics by applying sentence embeddings plus UMAP and HDBSCAN to abstracts. This mechanism discovers fine-grained topic clusters that traditional bag-of-words methods might miss in scientific literature.

How do I track dynamic topic trends and shifts across years in a corpus?▼

Track dynamic topic trends by using BERTopic's topics-over-time functionality on abstract-scale corpora. This visualizes topic evolution across publication years, revealing how dominant themes shift year over year in a specific field.

Does topic modeling with LDA support interactive visualization for publication-ready results?▼

Yes, LDA topic modeling supports interactive visualization by generating PyLDAvis HTML files. This creates an exportable, interactive visual output alongside human-readable topic summaries for the best coherence-selected model.

Do I need Python and specific NLP libraries to perform topic modeling on academic literature?▼

Yes, you need Python tooling including gensim for LDA, and bertopic with sentence-transformers for BERTopic modeling. Optional pyLDAvis is supported for generating interactive visualizations of the resulting topics.

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