empathy-clustering

Cluster emotional pain points from empathy maps using semantic embeddings.

Updated Apr 7, 2026

Search Tags:#pattern recognition #empathy #UX research #clustering #semantic embeddings #emotional analysis

Ready to run

npx skills add https://github.com/uh-joan/ux-research-skills --skill empathy-clustering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: empathy-clustering
Source: https://github.com/uh-joan/ux-research-skills/tree/main/.claude/skills/empathy-clustering
Command: npx skills add https://github.com/uh-joan/ux-research-skills --skill empathy-clustering

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sentence-transformers, hdbscan, umap-learn, scikit-learn, pandas, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automatically identifies emotional pain clusters within empathy maps using semantic embeddings, enabling CX teams to find common frustrations across users efficiently.

Core Features & Use Cases

Cross-user Emotional Pattern Recognition: Clusters empathy entries from multiple maps to reveal shared pain points.
Research Synthesis: Provides a structured clustering analysis to assist in cross-project synthesis.
Use Case: A UX researcher creates three empathy maps and uses this Skill to quickly identify the predominant feelings and frustrations across different user segments, guiding design decisions.

Quick Start

Use the empathy-clustering skill after generating empathy maps to analyze and discover emotional clusters across your projects.

Frequently Asked Questions about empathy-clustering

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

FAQPage Schema

How do I identify shared emotional pain points across multiple empathy maps?▼

You can identify shared emotional pain points across empathy maps by using semantic embeddings to process and cluster entries, automatically revealing common user frustrations across different UX research projects.

How does semantic clustering work for UX research synthesis?▼

Semantic clustering for UX research synthesis works by converting empathy map text entries into vector embeddings and grouping them with clustering algorithms to uncover overarching emotional patterns across user segments.

Can I use HDBSCAN and UMAP to cluster empathy map data?▼

Yes, this approach uses HDBSCAN and UMAP alongside sentence-transformers to process empathy map data, reducing dimensionality and grouping semantic embeddings to identify shared emotional pain points.

What is the best way to analyze cross-domain emotional patterns in UX research?▼

The best way to analyze cross-domain emotional patterns is to apply automated clustering to empathy map entries, synthesizing semantic embeddings to efficiently reveal shared frustrations across different user segments and projects.

Do I need sentence-transformers to process empathy map emotional analysis?▼

Yes, sentence-transformers are required to generate the semantic embeddings from empathy map entries, which are then processed by clustering algorithms to identify and group emotional pain points.

When should I not use automated clustering for empathy map analysis?▼

Automated clustering for empathy map analysis is not suited for small sets of unstructured entries lacking semantic depth, as clustering algorithms require sufficient text volume to identify meaningful shared emotional patterns.