What are 'Jobs to be Done' in this context?

JTBD identifies the core task or progress a specific user segment is trying to achieve, helping you understand the functional and emotional needs behind their feedback.

Can it help with product roadmap prioritization?

Yes, it categorizes and ranks insights based on frequency, severity, and business impact, offering specific 'quick wins' vs. strategic long-term recommendations.

How does it calculate sentiment scores?

It assigns an overall sentiment score ranging from -1 to +1 for each identified user segment based on the qualitative tone and frequency of feedback within the dataset.

Is this skill restricted to Chinese language data?

While the documentation is provided in Chinese, the underlying AI logic and the Claude Code environment can analyze feedback in almost any language.

What data formats does this skill support?

The sentiment-analysis skill can process CSV files, PDFs, survey responses, review data, and social listening reports provided as context to Claude.

Market Research Sentiment Analysis

Name: Market Research Sentiment Analysis
Author: killvxk

bykillvxk

•

데이터 과학 및 ML

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

This skill empowers product managers and researchers to transform raw qualitative data—such as CSVs, survey responses, and social listening reports—into structured market intelligence. It automatically segments users, evaluates sentiment on a -1 to +1 scale, and identifies Jobs to be Done (JTBD) to highlight critical pain points and growth opportunities. By synthesizing feedback into prioritized insights, it helps teams bridge the gap between user sentiment and strategic product development, making it ideal for large-scale feedback synthesis.

주요 기능

01Identification of Jobs to be Done (JTBD) and core user motivations

02Actionable product-market fit assessments and improvement roadmaps

0317 GitHub stars

04Sentiment scoring and NPS proxy calculation for specific user groups

05Automated segmentation of user feedback into distinct personas

06Prioritized pain point mapping based on frequency and business impact

사용 사례

01Processing qualitative survey data to identify feature requests and UX friction

02Monitoring social media sentiment to gauge product reception during a new feature launch

03Analyzing high volumes of app store reviews or customer support tickets to find patterns

주요 기능

01Identification of Jobs to be Done (JTBD) and core user motivations

02Actionable product-market fit assessments and improvement roadmaps

0317 GitHub stars

04Sentiment scoring and NPS proxy calculation for specific user groups

05Automated segmentation of user feedback into distinct personas

06Prioritized pain point mapping based on frequency and business impact

사용 사례

01Processing qualitative survey data to identify feature requests and UX friction

02Monitoring social media sentiment to gauge product reception during a new feature launch

03Analyzing high volumes of app store reviews or customer support tickets to find patterns