Community AnalysisCommunity Topic Analysis
Do timeline reconstruction and sentiment-arc analysis for a single hot topic, extracting opposing viewpoints, cross-channel differences, and operations recommendations.
Sentiment timeline
over timeThe Community Hot Topic Insight Skill is a hot-topic deep-insight capability in ThinkingAI's industry Skill library. It helps operations and community teams do timeline reconstruction and sentiment-arc analysis for a single hot topic, tracking the topic from ignition through peak, response, and decline, and extracting opposing viewpoints, representative quotes, cross-channel differences, and industry-specific recommendations. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.
Live walkthrough
Timeline traced. Key conclusion first: the trigger for the sentiment flip wasn't the character launch itself, but a KOL hands-on-test video on the evening of 6/14. The official announcement wasn't late, but it addressed the wrong point of contention.
1. Topic timeline and sentiment arc
2. Opinion clustering, for and against
| Stance | Core viewpoints (by cluster share) | Representative quote |
|---|---|---|
| Negative | Stats don't match the PV demo (34%) / gacha pricing too high (27%) / signature weapon boost too small (12%) | "In the PV one hit takes half the health bar; in the live build one hit does 8%" |
| Positive | Art and voice-acting quality (21%) / story line praised (14%) | "For this story alone, pulling was worth it" |
| Neutral | Wait for later tuning before deciding (the mainstream of post-announcement discussion) | "I'll take the compensation; we'll see the stats next version" |
3. Cross-channel differences
| Channel | Discussion share | Tone characteristics | Unique angle |
|---|---|---|---|
| 41% | Mostly emotional expression, spreads fast via reshares | Fan creations and memes amplify the "overstated stats" topic | |
| Tieba | 28% | Hardcore data critiques | Several frame-by-frame damage-calculation posts appeared |
| TapTap | 19% | Direct rating behavior | Rating dropped 0.4 within three days |
| NGA | 12% | Constructive discussion | Offered concrete stat-adjustment proposals |
4. Per-channel response recommendations
On your data
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The problem
Hot topics are a key signal for community operations, yet more than 70% of teams stop at "glancing at the trending rank": they don't know when a topic started building, at which node sentiment flipped, or how much platform reactions differed. Without timeline reconstruction you cannot see the sentiment-flip trigger or know whether the official response worked; and viewing Weibo, Tieba, and official-forum characteristics all mixed together masks differentiated response strategies.
What it does
When to use it
Tracking community topic evolution after a new game version or new character launches
Reconstructing the timeline to see the sentiment-flip point after an event announcement sparks controversy
Comparing how the same topic is received across different platforms
Extracting opposing viewpoints and representative user quotes for a hot topic
Building a crisis-response or positive-amplification strategy from topic insight
In the field
FAQ
How does it differ from the community daily/weekly report?
Topic insight does deep analysis of a single hot topic, while daily/weekly reports are all-platform time-dimension summaries. Topic insight is narrow and deep, daily/weekly reports are broad and fast.
Will the timeline be fabricated?
No. The timeline is based on real post timestamps obtained via MCP tools, and if a timestamp is missing it is clearly marked, with no fabricated history.
Which platforms' data are supported?
It supports mainstream community platforms including TapTap, Bilibili, Douyin, Weibo, Xiaohongshu, HeyBox, Douyu, Tieba, and NGA.
Related Skills
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