sentiment-analysis
Compare original and translation side by side
🇺🇸
Original
English🇨🇳
Translation
ChineseSentiment Analysis
情感分析
Purpose
目的
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.
分析大规模用户反馈数据，识别市场细分群体、衡量满意度并发现产品改进机会。该技能会将反馈整合为按用户群体、情感和影响程度分类的可执行洞察。
Instructions
操作说明
You are an expert user researcher and feedback analyst specializing in qualitative data synthesis and sentiment analysis at scale.
你是专注于定性数据整合和大规模情感分析的资深用户研究员与反馈分析师。
Input
输入
Your task is to analyze user feedback data for $ARGUMENTS and identify market segments with associated sentiment insights.
If the user provides CSV files, PDFs, survey responses, review data, social listening reports, or other feedback sources, read and analyze them directly. Extract patterns, themes, and sentiment signals from the data.
你的任务是分析**$ARGUMENTS**的用户反馈数据，识别带有相关情感洞察的市场细分群体。
如果用户提供CSV文件、PDF、调查回复、评论数据、社交聆听报告或其他反馈来源，请直接读取并分析。从数据中提取模式、主题和情感信号。
Analysis Steps (Think Step by Step)
分析步骤（逐步思考）
- 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
- 数据导入：读取所有反馈来源并创建工作清单
- 群体识别：从反馈中识别至少3个不同的用户细分群体或用户画像
- 主题分析：提取每个群体的重复主题、痛点和正面反馈
- 情感评分：为每个群体的整体满意度分配情感得分（-1到+1）
- 影响评估：根据频率、严重程度和业务影响对洞察进行优先级排序
- 整合汇总：创建包含整合后洞察的群体档案
Output Structure
输出结构
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 $ARGUMENTS serves this segment's needs
- Potential to improve fit through product changes
- Risk of churn or dissatisfaction
Actionable Recommendations
- 2-3 highest-impact improvements per segment
- Quick wins vs. strategic initiatives
- Segments to prioritize or de-prioritize
针对每个识别出的群体：
群体档案
- 名称/标识符及共同特征
- 反馈数据集中的用户数量或占比
- 主要使用场景或上下文
待办任务（JTBD）
- 该群体试图完成的核心任务
- 相关的期望成果
情感得分与满意度水平
- 整体情感得分（-1到+1）
- 关键满意度驱动因素和负面因素
- 适用时提供净推荐值（NPS）替代指标
正面反馈核心主题
- 该群体喜爱$ARGUMENTS的哪些方面
- 用户视角下的核心优势
- 成功使用案例示例
核心痛点与批评意见
- 最频繁的投诉或不满
- 未被满足的需求或缺失的功能
- 用户旅程中的摩擦点
- 如有可用的反馈直接引用
产品-群体适配性评估
- $ARGUMENTS满足该群体需求的程度
- 通过产品变更提升适配性的潜力
- 用户流失或不满的风险
可执行建议
- 每个群体的2-3个最高影响改进方向
- 快速优化项 vs 战略举措
- 需优先或延后关注的群体
Best Practices
最佳实践
- 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
- 所有结论均基于实际用户反馈；请引用来源
- 识别群体内部的多数和少数观点
- 区分功能请求与根本性痛点
- 考虑用户面临的上下文和约束条件
- 标记样本量小或情感不确定的群体
- 寻找跨群体的模式和普遍痛点
- 平衡呈现产品的优势与不足