sentiment-analysis

@phuryn · 收录于 1 周前 · 上游提交 1 周前★ 社区精选

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.

场景用户调研满意度分析评论分析

适合你，如果需要从大量用户反馈中提取情感洞察和满意度趋势。

/ 下载安装

https://github.com/phuryn/pm-skills/tree/main/pm-market-research/skills/sentiment-analysis ↗

/ 通过 npx 安装 校验哈希

npx oh-my-skill add phuryn/pm-skills/sentiment-analysis

/ 通过 bash 安装

curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- phuryn/pm-skills/sentiment-analysis

/ 已经装过?验证本机副本,不用重装

npx oh-my-skill verify phuryn/pm-skills/sentiment-analysis

安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。

23265GitHub stars

~668上下文体积 · 单文件

索引托管

怎么用

商店整理自技能原文 · 版本 18468a9 · 表述以原文为准

它做什么

装上后，Claude 会分析你提供的用户反馈数据（如评论、调查），识别不同用户群体，给出每个群体的满意度评分、痛点、改进建议。

什么时候触发

当你上传用户反馈文件（如 CSV、PDF）或粘贴评论、调查结果，并要求分析情感或满意度时触发。

装好后可以这样说

Claude 会读取文件并输出各群体的情感评分和洞察。

Claude 会提取负面主题并给出改进建议。

技能原文 SKILL.md作者撰写 · MIT · 18468a9

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