sentiment-analysis
phuryn/pm-skills
Analyze user feedback at scale to identify sentiment segments, satisfaction drivers, and product improvement opportunities.
What is sentiment-analysis?
This skill processes large volumes of user feedback data—surveys, reviews, social listening, and more—to identify distinct user segments with sentiment scores and satisfaction insights. Use it when you need to synthesize qualitative feedback into actionable product recommendations organized by user group and sentiment.
- Ingest and analyze feedback from CSV, PDF, survey, review, and social listening sources
- Identify 3+ distinct user segments or personas from feedback patterns
- Assign sentiment scores (-1 to +1) and satisfaction levels per segment
- Extract recurring themes, pain points, and positive feedback by segment
- Assess product-segment fit and prioritize improvement opportunities
- Generate segment profiles with Jobs-to-be-Done, NPS proxies, and actionable recommendations
How to install sentiment-analysis
npx skills add https://github.com/phuryn/pm-skills --skill sentiment-analysisHow to use sentiment-analysis
- 1.Prepare feedback data in CSV, PDF, survey response, or text format
- 2.Provide the data source(s) and specify what product or feature to analyze
- 3.The skill will ingest all feedback sources and create a working inventory
- 4.Review the generated segment profiles with sentiment scores, JTBD, and pain points
- 5.Use recommendations to prioritize product improvements by segment impact
Use cases
- Analyzing customer survey responses to identify satisfaction patterns across user groups
- Processing product reviews and social listening data to detect emerging pain points
- Synthesizing user interview transcripts to inform product roadmap prioritization
- Evaluating Net Promoter Score drivers by customer segment
- Identifying churn risk and feature request priorities from support feedback
- Product managers conducting user research and feedback analysis
- User researchers synthesizing qualitative data at scale
- Product teams prioritizing improvements based on customer sentiment
- Market researchers identifying segment-specific satisfaction drivers
sentiment-analysis FAQ
CSV files, PDFs, survey responses, review data, social listening reports, interview transcripts, and other text-based feedback sources.
The skill identifies at least 3 distinct segments based on patterns in your feedback data, but may identify more depending on data volume and diversity.
Sentiment scores range from -1 (very negative) to +1 (very positive), with 0 representing neutral. The skill also provides satisfaction levels and NPS proxies per segment.
Yes, but the skill will flag segments with small sample sizes or uncertain sentiment to help you interpret confidence levels appropriately.
Recommendations are prioritized by frequency, severity, and business impact. The skill distinguishes between quick wins and strategic initiatives per segment.
Full instructions (SKILL.md)
Source of truth, from phuryn/pm-skills.
name: sentiment-analysis description: "Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights. Use when analyzing user feedback at scale, running sentiment analysis on reviews or surveys, or identifying satisfaction patterns."
Sentiment Analysis
Purpose
Analyze large-scale user feedback data to identify market segments, measure satisfaction, and uncover product improvement opportunities. This skill synthesizes feedback into actionable insights organized by user segment, sentiment, and impact.
Instructions
You are an expert user researcher and feedback analyst specializing in qualitative data synthesis and sentiment analysis at scale.
Input
Your task is to analyze user feedback data for $ARGUMENTS and identify market segments with associated sentiment insights.
If the user provides CSV files, PDFs, survey responses, review data, social listening reports, or other feedback sources, read and analyze them directly. Extract patterns, themes, and sentiment signals from the data.
Analysis Steps (Think Step by Step)
- Data Ingestion: Read all feedback sources and create a working inventory
- Segment Identification: Identify at least 3 distinct user segments or personas from the feedback
- Thematic Analysis: Extract recurring themes, pain points, and positive feedback per segment
- Sentiment Scoring: Assign sentiment scores (-1 to +1) for overall satisfaction per segment
- Impact Assessment: Prioritize insights by frequency, severity, and business impact
- Synthesis: Create segment profiles with consolidated insights
Output Structure
For each identified segment:
Segment Profile
- Name/identifier and common characteristics
- User count or proportion in feedback dataset
- Primary use case or context
Jobs-to-be-Done
- Core job this segment is trying to accomplish
- Associated desired outcomes
Sentiment Score & Satisfaction Level
- Overall sentiment score (-1 to +1)
- Key satisfaction drivers and detractors
- Net Promoter Score (NPS) proxy if applicable
Top Positive Feedback Themes
- What this segment loves about $ARGUMENTS
- Key strengths from user perspective
- Examples of successful use cases
Top Pain Points & Criticism
- Most frequent complaints or frustrations
- Unmet needs or missing features
- Friction points in user journey
- Direct quotes from feedback when available
Product-Segment Fit Assessment
- How well $ARGUMENTS serves this segment's needs
- Potential to improve fit through product changes
- Risk of churn or dissatisfaction
Actionable Recommendations
- 2-3 highest-impact improvements per segment
- Quick wins vs. strategic initiatives
- Segments to prioritize or de-prioritize
Best Practices
- Ground all findings in actual user feedback; cite sources
- Identify both majority and minority perspectives within segments
- Distinguish between feature requests and fundamental pain points
- Consider context and constraints users face
- Flag segments with small sample sizes or uncertain sentiment
- Look for cross-segment patterns and universal pain points
- Provide balanced view of product strengths and weaknesses
Further Reading
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