sentiment-analysis
The sentiment-analysis skill processes large volumes of user feedback data from multiple sources to quantify satisfaction levels, identify distinct user segments, and extract actionable product insights. Use this skill when systematically analyzing customer reviews, survey responses, or social feedback to prioritize feature development, understand satisfaction drivers, and detect churn risks across different user groups.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/sentiment-analysis && cp -r /tmp/sentiment-analysis/pm-market-research/skills/sentiment-analysis ~/.claude/skills/sentiment-analysisSKILL.md
# 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) 1. **Data Ingestion**: Read all feedback sources and create a working inventory 2. **Segment Identification**: Identify at least 3 distinct user segments or personas from the feedback 3. **Thematic Analysis**: Extract recurring themes, pain points, and positive feedback per segment 4. **Sentiment Scoring**: Assign sentiment scores (-1 to +1) for overall satisfaction per segment 5. **Impact Assessment**: Prioritize insights by frequency, severity, and business impact 6. **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 - [Market Research: Advanced Techniques](https://www.productcompass.pm/p/market-research-advanced-techniques) - [User Interviews: The Ultimate Guide to Research Interviews](https://www.productcompass.pm/p/interviewing-customers-the-ultimate)
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Analyze and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.
Generate an Ansoff Matrix analysis mapping growth strategies across market penetration, market development, product development, and diversification. Use when considering growth options, planning market expansion, or evaluating strategic growth paths.
Identify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry strategy.
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
Brainstorm product ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints. Use when generating new feature ideas, brainstorming solutions for an identified opportunity, or ideating with a product trio.
Brainstorm feature ideas for a new product in initial discovery from PM, Designer, and Engineer perspectives. Use when starting product discovery for a new product, exploring features for a startup idea, or doing initial ideation.