Does this skill support both R and Python?

Yes, it provides specific guidance, code patterns, and library recommendations for both R (e.g., stm, tidytext) and Python (e.g., scikit-learn, HuggingFace, spaCy).

How does it ensure research validity?

The skill follows a mandatory 6-phase systematic workflow that includes human validation, sensitivity analysis, and diagnostic checks to ensure findings are robust and publication-ready.

What types of text analysis can I perform?

You can perform topic modeling (LDA, STM, BERTopic), dictionary-based sentiment analysis, supervised classification, word embeddings, and named entity recognition.

Can it help me choose the right method for my data?

Absolutely. Phase 0 of the workflow is dedicated to research design, where the agent helps you select the appropriate method and language based on your corpus size and research goals.

Computational Text Analyst

Name: Computational Text Analyst
Author: nealcaren

bynealcaren

•

数据科学与机器学习

Guides systematic computational text analysis for social science research using R or Python to produce publication-ready results.

This skill transforms Claude into an expert research assistant for sociology and social science text analysis, providing a structured framework for the entire research lifecycle. It streamlines complex workflows—from initial research design and corpus preparation to advanced modeling and validation—supporting both traditional statistical methods like LDA and STM in R and modern neural approaches like BERTopic and Transformers in Python. By enforcing a phased approach and emphasizing rigorous human validation, it ensures that automated text analysis leads to robust, reproducible, and sociologically meaningful findings suitable for academic publication.

主要功能

01Systematic validation and robustness check protocols

022 GitHub stars

03Automated project folder organization for reproducibility

04Dual-language support for R (stm, tidytext) and Python (spaCy, BERTopic)

05Phased research workflow from design to final output

06Guided method selection based on research questions

使用场景

01Performing neural sentiment analysis and classification using BERT models

02Executing Structural Topic Modeling (STM) on interview transcripts or policy documents

03Conducting systematic validation of algorithmic outputs against human-coded samples

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nealcaren/sociology-analysis-agents text-analyst

For use in Claude.ai and ChatGPT

Download Skill