# Sentiment Analysis Agent Skill > Analyze user feedback at scale with this sentiment analysis skill: segment users by sentiment and JTBD, get prioritized product insights. Start free in seconds. - Canonical: https://nanoskill.ai/nb/skills/sentiment-analysis-agent-skill - Markdown: https://nanoskill.ai/nb/skills/sentiment-analysis-agent-skill.md - Author: phuryn - Published: 2026-11-05T06:30:00.000Z - Updated: 2026-11-06T10:20:37.181Z - Language: nb - Source type: github - Popularity signal: 25845 ## Sources - https://github.com/phuryn/pm-skills/tree/main/pm-market-research/skills/sentiment-analysis ## Install ```shell npx skills add https://github.com/phuryn/pm-skills/tree/main/pm-market-research/skills/sentiment-analysis ``` ## About Sentiment analysis transforms raw user feedback into segmented product insights. Product managers, UX researchers, and founders use this skill to process reviews, surveys, and support data, then get clear segment profiles, satisfaction scores, and prioritized improvement opportunities. The skill's core capabilities include automatic segment identification, thematic analysis, sentiment scoring on a -1 to +1 scale, and impact prioritization. It goes beyond overall sentiment by connecting jobs-to-be-done, pain points, and product-segment fit so teams can see both what users love and what drives churn. Use it during roadmap planning, churn reviews, or post-launch feedback triage. Feed it app store reviews, survey exports, or social listening reports, and it returns segment-level recommendations that separate quick wins from strategic initiatives, with direct quotes when available. ## Key features - **Run sentiment analysis at scale**: Ingests CSV, PDF, survey, review, and social listening data and synthesizes feedback into clear segment profiles. - **Score sentiment by segment**: Assigns a -1 to +1 sentiment score and an NPS proxy per segment to quantify satisfaction and spot churn risk. - **Extract JTBD and pain points**: Pulls jobs-to-be-done, desired outcomes, top complaints, and unmet needs from qualitative feedback with direct quotes when available. - **Prioritize product improvements**: Ranks insights by frequency, severity, and business impact to separate quick wins from strategic initiatives. ## Use cases - **Analyze product reviews**: Product managers run sentiment analysis on app store, G2, or Trustpilot reviews to identify segment-level satisfaction drivers and criticism. - **Synthesize survey responses**: UX researchers turn open-ended survey answers into JTBD insights and prioritized pain points without manual tagging. - **Monitor social listening reports**: Marketers and community leads process mentions and feedback threads to detect early dissatisfaction and feature demand by segment. ## Result preview See a sentiment report with segment gauges, cross-segment pain points, and a build-first shortlist. ![the demo of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-demo-1.png) ![the demo of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-demo-2.png) ![the demo of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-demo-3.png) ![the demo of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-demo-4.png) ## Result walkthrough ### Install Add the Sentiment Analysis Skill to your AI agent. ![the step of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-step-1.png) ### Upload Feedback Data Provide reviews, surveys, or social listening exports. ![the step of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-step-2.png) ### Get Your Sentiment Report Receive behavior-based segments with sentiment scores and priorities. ![the step of sentiment analysis agent skill](https://file.nanoskill.ai/sentiment-analysis-agent-skill-step-3.png) ## Skill definition # 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) ## FAQ ### What is sentiment analysis for user feedback? It is a method of reading reviews, surveys, and support data to identify user segments and assign satisfaction scores, so you can see which groups love or struggle with a product. ### How does this skill analyze user feedback data? It follows six steps: data ingestion, segment identification, thematic analysis, sentiment scoring on a -1 to +1 scale, impact assessment, and synthesis into segment profiles and recommendations. ### What sentiment score does it use? Each segment gets an overall sentiment score from -1 to +1, with 1 for positive and -1 for negative. It also adds an NPS proxy when the data supports it. ### Which user segments does it identify? The skill identifies at least three distinct user segments or personas from the feedback, including their characteristics, proportion in the dataset, and primary context. ### Does it work with CSV, PDF, and survey data? Yes. It can read CSV files, PDFs, survey responses, review exports, social listening reports, and other feedback sources and analyze them directly. ### Can I use it for product reviews or social listening? Yes. The skill is designed for user feedback at scale, including reviews, surveys, and social listening reports, and it flags segments with small sample sizes or uncertain sentiment.