sentiment-analysis
npx machina-cli add skill phuryn/pm-skills/sentiment-analysis --openclawSentiment 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
Source
git clone https://github.com/phuryn/pm-skills/blob/main/pm-market-research/skills/sentiment-analysis/SKILL.mdView on GitHub Overview
Analyze large volumes of user feedback to identify market segments, JTBDs, and product satisfaction insights. It surfaces patterns and opportunities, organizing insights by segment, sentiment, and impact to guide prioritization and action.
How This Skill Works
The skill ingests feedback from CSVs, PDFs, surveys, reviews, or social listening reports, then identifies user segments and recurring themes. It assigns a sentiment score between -1 and +1 per segment, maps Jobs-to-be-Done (JTBD) and outcomes, assesses business impact, and synthesizes segment profiles with actionable recommendations.
When to Use It
- Analyzing customer feedback at scale to uncover segment-level insights
- Running sentiment analysis on reviews or survey responses to quantify satisfaction
- Identifying satisfaction patterns and JTBD-driven outcomes across user groups
- Prioritizing product improvements based on segment impact and sentiment
- Benchmarking sentiment and satisfaction trends over time to track progress
Quick Start
- Step 1: Ingest feedback data from CSV, PDF, survey responses, and reviews
- Step 2: Identify at least 3 segments and extract recurring themes and JTBD per segment
- Step 3: Compute -1 to +1 sentiment per segment and craft segment profiles with recommendations
Best Practices
- Ground findings in actual user feedback and cite sources
- Identify both majority and minority perspectives within segments
- Differentiate feature requests from fundamental pain points
- Contextualize feedback with user constraints and usage scenarios
- Flag segments with small samples or uncertain sentiment
Example Use Cases
- Segment A (onboarding-new users) reports high friction and negative sentiment, guiding onboarding improvements
- Segment B (power users) shows strong positive sentiment tied to advanced features, informing feature prioritization
- Negative feedback clusters around performance issues during peak times, prompting reliability fixes
- Surveys reveal cross-cutting pain points in mobile UX, suggesting a broader UX redesign
- NPS proxy indicates overall satisfaction trend aligned with product updates, guiding release timing
