Topic Analysis

---
name: topic-analysis
description: Use when analyzing content topics on Xiaohongshu, identifying trending themes, researching audience interests, evaluating topic performance, or planning content around high-engagement topics
---

# Topic Analysis (话题分析)

## Overview

Topic analysis is the systematic examination of content themes, subject matters, and conversation trends on Xiaohongshu to understand what resonates with audiences, identify emerging opportunities, and make data-driven content planning decisions.

## When to Use

- Identifying trending topics and themes
- Analyzing which topics drive engagement
- Researching audience interests and preferences
- Evaluating content topic performance over time
- Planning content around proven themes
- Discovering content gaps and opportunities
- Measuring brand topic alignment

## Core Pattern

**Before**: Random content, guessing topics, inconsistent themes
**After**: Data-driven topics, proven themes, strategic content

**5 Topic Dimensions**:
1. Performance Topics (high engagement, proven winners)
2. Trending Topics (rising popularity, time-sensitive)
3. Evergreen Topics (consistent performance, reliable)
4. Niche Topics (underserved, opportunity-rich)
5. Brand Topics (core to brand identity, strategic)

## Quick Reference

| Topic Type | Engagement | Competition | Longevity | Best For |
|------------|------------|-------------|-----------|----------|
| Performance | High | Varies | Varies | Capitalize on success |
| Trending | Spike | Low-Medium | Short | Timely content |
| Evergreen | Consistent | High | Long | Sustainable growth |
| Niche | Medium | Low | Medium | Differentiation |
| Brand | Building | Varies | Long | Brand positioning |

## Implementation

### Step 1: Collect Topic Data

**Data Sources**:
- Your content performance by topic
- Competitor topic analysis
- Platform trending topics
- Audience questions and requests
- Search trends and suggestions
- Seasonal topic patterns

**Build Topic Database**:
Track: Topic name, post count, avg engagement, trend direction, last covered, priority

### Step 2: Analyze Topic Performance

**Topic Metrics**:
- Engagement rate per topic
- Reach and impressions
- Follower growth by topic
- Save and share rates
- Comment sentiment by topic
- Conversion rate by topic

**Performance Categories**:
- Star topics (top 10% performers)
- Strong topics (top 25%)
- Average topics (middle 50%)
- Weak topics (bottom 25%)
- Avoid topics (consistently underperform)

### Step 3: Identify Topic Trends

**Trend Analysis**:
- Rising topics (momentum increasing)
- Stable topics (consistent performance)
- Declining topics (losing interest)
- Seasonal patterns (predictable cycles)
- Viral spikes (sudden popularity)

**Trend Detection**:
- Week-over-week change
- Month-over-month change
- Seasonal comparison (same period last year)
- Platform trend alignment

### Step 4: Map Topics to Content Strategy

**Topic Allocation**:
- 40% Star topics (proven winners)
- 30% Trending topics (timely relevance)
- 20% Evergreen topics (consistency)
- 10% Experimental topics (innovation)

**Content Calendar Integration**:
- Schedule star topics during peak times
- Plan trending topics while hot
- Evergreen topics for consistency
- Test topics in low-risk time slots

### Step 5: Monitor Topic Saturation

**Saturation Indicators**:
- Declining engagement on topic
- Increased competition
- Audience fatigue (comments like "again?")
- Diminishing returns over time

**Refresh Strategy**:
- New angle on same topic
- Different format (video vs post)
- Update with new information
- Pause over-saturated topics

## Real-World Impact

**Topic Analysis Results**:
- Content engagement +40% from topic optimization
- Follower growth +60% from trending topics
- Saved 50% time on content planning
- Identified 15 underserved niche topics

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## Related Skills

**REQUIRED**: Use data-analytics (quantitative analysis)
**REQUIRED**: Use content-performance-analysis (topic-specific metrics)

**Recommended**:
- trend-analysis, audience-research, content-strategy