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alterlab-digital-humanities

Apply computational text mining and topic modeling to humanities corpora.

58|9|Updated Mar 16, 2026
Search Tags:#nlp#text-mining#gis#stylometry#digital-humanities#corpus-linguistics
One-click install
npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-digital-humanities
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alterlab-digital-humanities
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/domain-specific/alterlab-digital-humanities
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-digital-humanities

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Digital humanities researchers often struggle to integrate computational methods into historical and literary studies, limiting scalable analysis and reproducibility.

Core Features & Use Cases

  • Text mining and NLP for humanities (topic modeling, named entity recognition, sentiment analysis, concordance)
  • Corpus linguistics and metadata standards (Dublin Core, TEI XML, data visualization)
  • GIS, network analysis, stylometry, OCR workflows, and digital editions across humanities domains
  • Example: apply topic modeling to a large corpus of Victorian novels to identify dominant themes and track their evolution over time

Quick Start

Provide a small English text corpus and run a basic topic model (BERTopic) to explore themes.

Frequently Asked Questions about alterlab-digital-humanities

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

FAQPage Schema
How do I apply NLP and text mining to large humanities corpora?▼

Topic modeling identifies dominant themes across large text corpora and tracks their evolution over time. You provide a text corpus and run a model like BERTopic to explore latent semantic structures computationally.

Can I use GIS and network analysis for historical literary research?▼

Stylometry and corpus linguistics analyze large text collections to quantify authorial style, vocabulary patterns, and linguistic variations. These methods support reproducible computational analysis across humanities domains without manual reading.

What is the best way to manage metadata standards for digital editions?▼

Metadata standards like Dublin Core and TEI XML structure digital editions and corpus metadata for interoperability. They ensure consistent data visualization and reproducible computational analysis across humanities research projects.

Do I need OCR workflows for humanities text corpus processing?▼

OCR workflows convert scanned historical documents into machine-readable text for corpus processing. They are necessary when your source materials are physical archives or image-based PDFs requiring digitization before NLP analysis.

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