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survey-nlp-analyzer

Identify themes and sentiment in open-ended survey text.

Updated Mar 18, 2026
Search Tags:#nlp#text-mining#clustering#topic-modeling#sentiment-analysis#feedback-analysis#open-text
One-click install
npx skills add https://github.com/jac007x/CheatCodes-Skill-Library --skill survey-nlp-analyzer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: survey-nlp-analyzer
Source: https://github.com/jac007x/CheatCodes-Skill-Library/tree/main/survey-nlp-analyzer
Command: npx skills add https://github.com/jac007x/CheatCodes-Skill-Library --skill survey-nlp-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing large bodies of open-ended text is time-consuming and non-deterministic. This Skill standardizes an end-to-end NLP pipeline to extract topics, sentiment, quotes, and actionable insights from any text source.

Core Features & Use Cases

  • Topic modeling (NMF) and dictionary-driven assignments to reveal themes across surveys, feedback, and transcripts.
  • Sentiment overlay and quote curation to surface representative lines with context.
  • Dimensional breakdowns by user-defined context dimensions to enable cross-group comparisons and executive summaries.
  • Reproducible, parameterized intake and configurable outputs (HTML reports, dashboards, and CSV bundles).

Quick Start

Provide a corpus file and run the analyzer to generate topics, sentiment, quotes, and a shareable HTML report.

Frequently Asked Questions about survey-nlp-analyzer

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

FAQPage Schema
How do I analyze open-ended survey responses for themes and sentiment?▼

Topic modeling extracts themes from open-text by mapping responses to topics using matrix factorization or a predefined topic dictionary, then overlaying sentiment to surface representative quotes with contextual breakdowns.

Can I cross-tabulate NLP sentiment results by different demographic groups?▼

Yes, you can cross-tabulate sentiment results by defining optional context dimensions during intake, enabling cross-group comparisons and executive summaries across different user segments within your text corpus.

What is the best way to extract topics from anonymous pulse survey text?▼

The best way to extract topics from pulse survey text is applying topic modeling to map responses to themes, optionally using a predefined topic dictionary, then overlaying sentiment to curate representative quotes with context.

Do I need a predefined topic dictionary to cluster open-text feedback?▼

You do not need a predefined topic dictionary to cluster feedback; the analyzer can generate topics automatically using NMF modeling, but supplying one allows dictionary-driven assignments for more targeted thematic extraction.

What file formats and inputs are required to start text mining survey verbatims?▼

Text mining survey verbatims requires a corpus file containing a text column, a specified source type, and context dimensions, alongside optional configurations for anonymization level, number of topics, and output destination.

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