What types of data can this skill analyze?

The skill can process various feedback sources including CSV files, PDFs, survey responses, app reviews, and social listening reports.

Does it create user personas?

Yes, the skill identifies at least three distinct user segments or personas based on common characteristics and feedback patterns found in the dataset.

Is it integrated with Jobs-to-be-Done (JTBD) frameworks?

Yes, it specifically identifies the core jobs users are trying to accomplish and their associated desired outcomes for each segment.

How are sentiment scores calculated?

It assigns a numerical score from -1 (very negative) to +1 (very positive) by analyzing the tone and themes present in the qualitative data.

Can this skill help with roadmap prioritization?

Absolutely. It provides an impact assessment and actionable recommendations, ranking improvements by their potential business impact and user need.

Sentiment Analysis & User Feedback Insights

Name: Sentiment Analysis & User Feedback Insights
Author: KoryakinYurij

byKoryakinYurij

0•

데이터 과학 및 ML

Analyzes large-scale user feedback to identify market segments, sentiment scores, and actionable product improvement opportunities.

This skill empowers product managers and researchers to synthesize qualitative data from surveys, reviews, and reports into structured, actionable insights. By leveraging advanced thematic analysis and sentiment scoring, it identifies distinct user personas, maps their Jobs-to-be-Done, and quantifies satisfaction levels across different segments. It transforms raw feedback into a prioritized strategic roadmap, helping teams bridge the gap between user pain points and product development.

주요 기능

01Quantified sentiment scoring ranging from -1 to +1 across user groups

02Prioritized recommendations based on frequency, severity, and business impact

03Automated segment and persona identification from diverse feedback datasets

040 GitHub stars

05Jobs-to-be-Done (JTBD) mapping for specific market segments

06Thematic extraction of key pain points, strengths, and feature requests

사용 사례

01Analyzing social listening reports to evaluate product-market fit for new feature launches

02Processing thousands of App Store or Play Store reviews to uncover recurring friction points

03Synthesizing quarterly NPS survey results into segmented product improvement plans

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add koryakinyurij/self-sustain-system sentiment-analysis

For use in Claude.ai and ChatGPT

주요 기능

01Quantified sentiment scoring ranging from -1 to +1 across user groups

02Prioritized recommendations based on frequency, severity, and business impact

03Automated segment and persona identification from diverse feedback datasets

040 GitHub stars

05Jobs-to-be-Done (JTBD) mapping for specific market segments

06Thematic extraction of key pain points, strengths, and feature requests

사용 사례

01Analyzing social listening reports to evaluate product-market fit for new feature launches

02Processing thousands of App Store or Play Store reviews to uncover recurring friction points

03Synthesizing quarterly NPS survey results into segmented product improvement plans

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add koryakinyurij/self-sustain-system sentiment-analysis

For use in Claude.ai and ChatGPT