Community Analysis

Community Comment Analysis

Do deep theme analysis of a specific post or video's comment section, extracting viewpoint distribution, sentiment trends, opposing stances, and representative quotes.

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The Community Theme Comment Analysis Skill is a community-comment deep-analysis capability in ThinkingAI's industry Skill library. It helps operations and community teams do structured analysis of a specific post or video's comment section, covering viewpoint clustering and distribution, sentiment-trend tracking, opposing-stance comparison, and representative-quote extraction, and output a deep theme-comment analysis report. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.

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The comment section on that TapTap new-version update announcement blew up, and the team is saying the mood is bad and we should put out a broad reassurance notice. Before we send it, break the comments down for me: what exactly are people angry about, and are the angry ones actually the majority?

Locate the target post: the TapTap new-version update announcement

Post matched, 350 comments total

Page through the comments and filter low-value content (spam / emoji-only / system messages)

Kept 210 valid comments, 40% filtered out

Opinion clustering: merge themes and compute shares

Sentiment labeling and representative-quote extraction

45% positive / 35% negative / 20% neutral

The comment section is broken down, and the conclusion is the opposite of the team's gut feel: this isn't a one-sided pan: 45% positive, 35% negative, and the negatives are highly concentrated on one solvable problem, 'dailies are too grindy.' The 'very negative' impression comes mainly from the 40% of spam and emoji flooding; after filtering, the mood is clear.

1. Comment clusters and representative quotes

Theme	Share	Sentiment	Representative quote
Praise for the new roguelike mode	27%	Positive	'This mode is more fun than the main story; the devs actually stepped up this time.'
Thumbs-up for the new hero design	18%	Positive	'The kit is finally not just stitched-together numbers.'
Dailies are too grindy	21%	Negative	'Clearing dailies takes 40 minutes; working people really can't keep up.'
Not enough materials for light spenders	14%	Negative	'Without spending you're starved for materials, stuck to the point of quitting.'
Wait-and-see / other	20%	Neutral	'I'll watch for two weeks before deciding whether to come back.'

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2. Cluster-share comparison

Valid-comment cluster shares (n = 210)　Unit: %

The two negative clusters total 35%, of which 'dailies are too grindy' alone is 21%, the single largest negative source; 'not enough materials for light spenders' at 14% is next. On the positive side, the roguelike mode (27%) is the largest cluster of all: the version's core reputation is actually positive, skewed by the perception from negative spam.

Recommendation

Instead of a 'broad reassurance notice,' turn it into three targeted actions: (1) respond directly to daily task length, with a reduction plan and a ship date; (2) bridge the light-spender material gap with a limited-time supply event first, and adjust drop rates next version; (3) have the official account pin and repost the roguelike praise to amplify it. Solving the specific problem for that 21% cluster beats reassuring everyone.

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The problem

Community comments are the core source of real user feedback, yet more than 80% of operations teams judge the comment section by feel, not knowing the split of positive versus negative viewpoints, how sentiment is trending, or what the core demand is, with a misjudgment rate above 50%. Comment sections also often have 30% to 40% low-value content like spam, emoji, and system messages, and without filtering it is hard to extract real viewpoints.

What it does

Three-step workflow: locate the target post or video, paginate through comments and filter out low-value content, and do deep analysis of trends, viewpoints, and an overall conclusion

Low-value comment filtering: automatically filter spam, pure emoji, comments with fewer than 3 meaningful characters, and system messages

Viewpoint clustering and opposing comparison: preserve the two opposing stances, each viewpoint with a match count and representative quotes

Sentiment-trend tracking: tabulate daily comment volume and sentiment-distribution changes, marking sentiment inflection points and trigger events

Supports TapTap, Bilibili, Douyin, Weibo, Xiaohongshu, HeyBox, Douyu, Tieba, NGA, and other platforms

When to use it

A hot post's comment section needs deep viewpoint analysis

Tracking the comment-section sentiment trend after a video is published

Understanding users' opposing stances and core demand on a topic

Assessing content performance and user emotional state

Extracting representative quotes from comments as a basis for operations decisions

In the field

Case

An SLG game · new-version announcement comment analysis

The TapTap update announcement drew heavy comment feedback and the team assumed by feel it was "very negative." The Skill fetched 350 comments and, after filtering, kept 210 valid ones, finding 45% positive viewpoints (praise for the roguelike mode and the new hero design), 35% negative (daily tasks too grindy, insufficient low-spend materials), and 20% neutral. The operations strategy shifted from blanket reassurance to targeted optimization of the daily-task length.

FAQ

How does it differ from the community daily/weekly report?

Comment analysis does deep analysis of a single post or video, while daily/weekly reports are all-platform time-dimension summaries. The former is deep and narrow, the latter broad and fast.

How is a low-value comment defined?

It includes spam/duplicates, pure emoji or punctuation, comments with fewer than 3 meaningful characters, and system or official automated messages.

What if there are few comments?

When there are fewer than 10 valid comments, it warns that the sample is too thin and suggests choosing another target, rather than forcing out an unreliable conclusion.