survey-nlp-analyzer

Name: survey-nlp-analyzer
Availability: InStock
Author: jac007x

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

Ready to run

npx skills add https://github.com/jac007x/CheatCodes-Skill-Library --skill survey-nlp-analyzer

💬 Prompt for ChatGPT / Claude Chat▼

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.