/sentiment-monitoring
Use when monitoring brand sentiment on Xiaohongshu, tracking public opinion, managing reputation, responding to negative feedback, analyzing brand perception trends, or handling PR crises
$ npx -y skills add vivy-yi/xiaohongshu-skills --skill sentiment-monitoring --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/sentiment-monitoring
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when monitoring brand sentiment on Xiaohongshu, tracking public opinion, managing reputation, responding to negative feedback, analyzing brand perception trends, or handling PR crises
SKILL.md
sentiment-monitoring.SKILL.mdname: sentiment-monitoring
description: Use when monitoring brand sentiment on Xiaohongshu, tracking public opinion, managing reputation, responding to negative feedback, analyzing brand perception trends, or handling PR crises
Sentiment Monitoring (舆情监控)
Overview
Sentiment monitoring is the systematic tracking and analysis of brand mentions, comments, and conversations on Xiaohongshu to understand public perception, identify reputation risks early, and respond proactively to maintain positive brand image.
When to Use
**Use when**:
- Monitoring brand health and public perception
- Tracking sentiment during marketing campaigns
- Identifying and addressing negative feedback early
- Managing PR crises or reputation issues
- Analyzing competitor sentiment and positioning
- Measuring brand sentiment changes over time
- Responding to customer complaints or concerns
**Do NOT use when**:
- Monitoring basic engagement metrics (use data-analytics)
- Tracking daily content performance (use data-analytics)
- Analyzing competitor content strategies (use competitive-analysis)
Core Pattern
**Before** (blind to public opinion):
❌ "No idea what people are saying about our brand"
❌ "Negative comment goes viral before we notice"
❌ "Can't measure if our campaigns improved perception"
❌ "Reputation crisis catches us completely off guard"
**After** (proactive sentiment management):
✅ "Real-time alerts: sentiment dropped 15%, investigate immediately"
✅ "Identified negative trend emerging, addressed before crisis"
✅ "Campaign improved sentiment from 65% to 82% positive"
✅ "Comprehensive sentiment dashboard informs every decision"
**3-Level Monitoring Framework**: 1. **Monitoring** - Track mentions, comments, conversations in real-time 2. **Analysis** - Classify sentiment, identify themes, track trends 3. **Response** - Engage proactively, address issues, amplify positives
Quick Reference
| Sentiment Type | Indicators | Benchmark | Response Priority | |----------------|------------|-----------|-------------------| | **Positive** | Praise, recommendations, love | Target >75% | Amplify, thank users | | **Neutral** | Questions, information-seeking | 15-25% | Inform, educate | | **Negative** | Complaints, criticism, issues | <15% | Address within 24hrs | | **Crisis** | Viral negative, PR nightmare | <5% | Immediate response |
Implementation
Step 1: Set Up Monitoring Infrastructure
**Define Monitoring Scope**:
What to Monitor:
1. Brand Mentions
- Official brand name and variations
- Product names and variations
- Hashtags associated with brand
- Account handles and nicknames
2. Executive and Spokespeople
- Founder, CEO, key spokespeople
- Influencers representing brand
- Brand ambassadors and partners
3. Product Categories
- Category terms where brand competes
- Generic product terms + brand
- Competitor mentions for comparison
4. Campaign Monitoring
- Campaign-specific hashtags and keywords
- Campaign launch to 30 days post-end
- Influencer campaign mentions
Monitoring Frequency:
- Real-time: Crisis situations, major campaigns
- Hourly: During active campaigns or launches
- Daily: Standard brand health monitoring
- Weekly: Competitive analysis, trend reporting
**Monitoring Tools Setup**:
Platform Native Tools:
✅ Xiaohongshu Professional Account Analytics
- Track comment sentiment on your posts
- Monitor mention notifications
- Review direct messages and inquiries
✅ Native Search and Alerts
- Save searches for brand name
- Monitor hashtag usage
- Track location-based mentions
Third-Party Tools:
✅ Huitun Data (灰豚数据)
- Brand mention tracking
- Sentiment analysis over time
- Competitor comparison
✅ Chanmama (蝉妈妈)
- Real-time comment monitoring
- Influencer sentiment tracking
- Crisis alert system
✅ Social Listening Platforms
- Mention.com, Brandwatch, etc.
- Cross-platform sentiment tracking
- Automated alerts and reports
Custom Monitoring:
✅ Manual review spreadsheet
- Daily sentiment logging
- Issue categorization
- Response tracking
✅ Google Alerts for web mentions
- News articles featuring brand
- Blog posts and reviews
- Forum discussions
Step 2: Establish Sentiment Baseline
**Measure Current Brand Sentiment**:
Baseline Metrics to Track:
1. Overall Sentiment Score
- Percentage of positive vs negative mentions
- Track over time (weekly, monthly)
- Benchmark: >75% positive is healthy
2. Sentiment by Content Type
- Product posts sentiment
- Educational content sentiment
- Promotional content sentiment
- Personal/lifestyle content sentiment
3. Sentiment by Audience Segment
- New followers vs long-time followers
- Different age demographics
- Different geographic regions
4. Sentiment Drivers
- What topics generate positive sentiment?
- What issues trigger negative sentiment?
- What questions or concerns come up repeatedly?
Baseline Data Collection:
- Analyze last 30 days of comments and mentions
- Classify minimum 500 comments as positive/neutral/negative
- Identify top themes and topics
- Calculate baseline sentiment percentages
**Sentiment Classification Guide**:
Positive Indicators (友好的):
✅ Praise and compliments: "太棒了", "很喜欢", "好漂亮"
✅ Recommendations: "强烈推荐", "姐妹们快冲", "种草了"
✅ Purchase intent: "已经下单", "想要", "在哪里买"
✅ Gratitude: "谢谢分享", "学到了", "很有帮助"
✅ Emotional connection: "爱了爱了", "太心动了", "必须支持"
✅ Positive emojis: ❤️, 🔥, 👍, 😍, ✨
Neutral Indicators (中性的):
❓ Questions: "请问", "怎么", "多少钱", "哪里买"
❓ Information-seeking: "有没有", "是什么", "怎么样"
❓ Tagging friends: "@某某 看看", "@闺蜜"
❓ Factual comments: "收到", "知道了", "好的"
Negative Indicators (负面的):
⚠️ Complaints: "不好用", "踩雷", "失望"
⚠️ Criticism: "太贵了", "不推荐", "别买"
⚠️ Issues: "质量差", "客服差", "物流慢"
⚠️ Comparison: "还是XX品牌好", "不如XX"
⚠️ Skepticism: "广告吧", "像是假的", "不可信"
⚠️ Negative emojis: 👎, 😡, 😞, 💔
Crisis Indicators (危机级别):
🚨 Viral ne
Read more
name: sentiment-monitoring description: Use when monitoring brand sentiment on Xiaohongshu, tracking public opinion, managing reputation, responding to negative feedback, analyzing brand perception trends, or handling PR crises
Sentiment Monitoring (舆情监控)
Overview
Sentiment monitoring is the systematic tracking and analysis of brand mentions, comments, and conversations on Xiaohongshu to understand public perception, identify reputation risks early, and respond proactively to maintain positive brand image.
When to Use
**Use when**:
- Monitoring brand health and public perception
- Tracking sentiment during marketing campaigns
- Identifying and addressing negative feedback early
- Managing PR crises or reputation issues
- Analyzing competitor sentiment and positioning
- Measuring brand sentiment changes over time
- Responding to customer complaints or concerns
**Do NOT use when**:
- Monitoring basic engagement metrics (use data-analytics)
- Tracking daily content performance (use data-analytics)
- Analyzing competitor content strategies (use competitive-analysis)
Core Pattern
**Before** (blind to public opinion):
❌ "No idea what people are saying about our brand" ❌ "Negative comment goes viral before we notice" ❌ "Can't measure if our campaigns improved perception" ❌ "Reputation crisis catches us completely off guard"
**After** (proactive sentiment management):
✅ "Real-time alerts: sentiment dropped 15%, investigate immediately" ✅ "Identified negative trend emerging, addressed before crisis" ✅ "Campaign improved sentiment from 65% to 82% positive" ✅ "Comprehensive sentiment dashboard informs every decision"
**3-Level Monitoring Framework**: 1. **Monitoring** - Track mentions, comments, conversations in real-time 2. **Analysis** - Classify sentiment, identify themes, track trends 3. **Response** - Engage proactively, address issues, amplify positives
Quick Reference
| Sentiment Type | Indicators | Benchmark | Response Priority | |----------------|------------|-----------|-------------------| | **Positive** | Praise, recommendations, love | Target >75% | Amplify, thank users | | **Neutral** | Questions, information-seeking | 15-25% | Inform, educate | | **Negative** | Complaints, criticism, issues | <15% | Address within 24hrs | | **Crisis** | Viral negative, PR nightmare | <5% | Immediate response |
Implementation
Step 1: Set Up Monitoring Infrastructure
**Define Monitoring Scope**:
What to Monitor: 1. Brand Mentions - Official brand name and variations - Product names and variations - Hashtags associated with brand - Account handles and nicknames 2. Executive and Spokespeople - Founder, CEO, key spokespeople - Influencers representing brand - Brand ambassadors and partners 3. Product Categories - Category terms where brand competes - Generic product terms + brand - Competitor mentions for comparison 4. Campaign Monitoring - Campaign-specific hashtags and keywords - Campaign launch to 30 days post-end - Influencer campaign mentions Monitoring Frequency: - Real-time: Crisis situations, major campaigns - Hourly: During active campaigns or launches - Daily: Standard brand health monitoring - Weekly: Competitive analysis, trend reporting
**Monitoring Tools Setup**:
Platform Native Tools: ✅ Xiaohongshu Professional Account Analytics - Track comment sentiment on your posts - Monitor mention notifications - Review direct messages and inquiries ✅ Native Search and Alerts - Save searches for brand name - Monitor hashtag usage - Track location-based mentions Third-Party Tools: ✅ Huitun Data (灰豚数据) - Brand mention tracking - Sentiment analysis over time - Competitor comparison ✅ Chanmama (蝉妈妈) - Real-time comment monitoring - Influencer sentiment tracking - Crisis alert system ✅ Social Listening Platforms - Mention.com, Brandwatch, etc. - Cross-platform sentiment tracking - Automated alerts and reports Custom Monitoring: ✅ Manual review spreadsheet - Daily sentiment logging - Issue categorization - Response tracking ✅ Google Alerts for web mentions - News articles featuring brand - Blog posts and reviews - Forum discussions
Step 2: Establish Sentiment Baseline
**Measure Current Brand Sentiment**:
Baseline Metrics to Track: 1. Overall Sentiment Score - Percentage of positive vs negative mentions - Track over time (weekly, monthly) - Benchmark: >75% positive is healthy 2. Sentiment by Content Type - Product posts sentiment - Educational content sentiment - Promotional content sentiment - Personal/lifestyle content sentiment 3. Sentiment by Audience Segment - New followers vs long-time followers - Different age demographics - Different geographic regions 4. Sentiment Drivers - What topics generate positive sentiment? - What issues trigger negative sentiment? - What questions or concerns come up repeatedly? Baseline Data Collection: - Analyze last 30 days of comments and mentions - Classify minimum 500 comments as positive/neutral/negative - Identify top themes and topics - Calculate baseline sentiment percentages
**Sentiment Classification Guide**:
Positive Indicators (友好的): ✅ Praise and compliments: "太棒了", "很喜欢", "好漂亮" ✅ Recommendations: "强烈推荐", "姐妹们快冲", "种草了" ✅ Purchase intent: "已经下单", "想要", "在哪里买" ✅ Gratitude: "谢谢分享", "学到了", "很有帮助" ✅ Emotional connection: "爱了爱了", "太心动了", "必须支持" ✅ Positive emojis: ❤️, 🔥, 👍, 😍, ✨ Neutral Indicators (中性的): ❓ Questions: "请问", "怎么", "多少钱", "哪里买" ❓ Information-seeking: "有没有", "是什么", "怎么样" ❓ Tagging friends: "@某某 看看", "@闺蜜" ❓ Factual comments: "收到", "知道了", "好的" Negative Indicators (负面的): ⚠️ Complaints: "不好用", "踩雷", "失望" ⚠️ Criticism: "太贵了", "不推荐", "别买" ⚠️ Issues: "质量差", "客服差", "物流慢" ⚠️ Comparison: "还是XX品牌好", "不如XX" ⚠️ Skepticism: "广告吧", "像是假的", "不可信" ⚠️ Negative emojis: 👎, 😡, 😞, 💔 Crisis Indicators (危机级别): 🚨 Viral ne
版本: v3.0 Complete Edition 更新: 2025-01-22 状态: ✅ 完整 (139个技能)
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