Douyin Comment Insight Analysis

Problem being solved

Useful signals in Douyin comments are often scattered across many short texts: users may mention needs, usage blockers, competitor comparisons, after-sales issues, or content suggestions. These are mixed with likes, replies, spam, and noisy threads, so screenshots or manual reading make it hard to form a stable judgment. This skill is better suited for turning comment text and engagement fields into an auditable report, not just sentiment classification.

How it works and boundaries

The skill first confirms scope, time window, comment level, target volume, sorting rules, brand/competitor terms, and whether reply suggestions are needed. Data sources prefer a configured comment connector or official API, then a user-logged-in page, then user-exported files; it does not bypass CAPTCHAs, login limits, or bulk-scraping limits.

The workflow includes:
- collecting fields such as comment ID, video ID, raw text, publish time, likes, replies, and parent comment ID;
- deduplicating by comment ID or normalized text with confidence labels;
- preserving source evidence while marking ads, spam, non-semantic emojis, and suspected bot content;
- classifying emotion, demand, pain point, purchase intent, after-sales issue, competitor mention, risk, and content suggestion;
- ranking high-value signals by likes, replies, and topic frequency, then outputting short excerpts, priorities, and suggested actions.

The output includes sample overview, topic distribution, demand and pain points, competitor and reputation signals, high-value comments, and an action list. It uses anonymous sample IDs by default, does not force sarcasm, rhetorical questions, or dialect into definitive sentiment, and states sample boundaries when pagination, login, or API limits apply rather than claiming full-volume coverage.