企业尽调舆情采集——通过抖音指数、抖音精选、WebSearch 三大数据源采集企业舆情，分析负面风险（诉讼/处罚/质量/高管/劳资），输出尽调舆情报告。Use when 用户提到"查舆情"、"企业舆情"、"负面新闻"、"舆论风险"、"尽调舆情"、"企业口碑"、"舆情分析"、"网络口碑"。
1.0.0
procedural
medium
enabled
due-diligence
sentiment
public-opinion
douyin
author
updated_at
i18n
desirecore
2026-09-03
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zh-CN
en-US
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企业尽调舆情采集
抖音指数+抖音精选+WebSearch 三渠道采集，输出尽调舆情报告
企业尽调舆情采集——通过抖音指数、抖音精选、WebSearch 三大数据源采集企业舆情，分析负面风险（诉讼/处罚/质量/高管/劳资），输出尽调舆情报告。Use when 用户提到"查舆情"、"企业舆情"、"负面新闻"、"舆论风险"、"尽调舆情"、"企业口碑"、"舆情分析"、"网络口碑"。
./SKILL.zh-CN.md
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Multi-Source Sentiment
Due-diligence sentiment via Douyin Index, Douyin Featured, and WebSearch
Due-diligence sentiment collection — gather enterprise public opinion via three channels (Douyin Index, Douyin Featured, WebSearch), analyze negative risks (litigation / penalties / quality / executives / labor), and output a due-diligence sentiment report. Use when the user asks about enterprise sentiment, negative news, reputation risk, or public opinion.
Input a company name, collect public opinion via Douyin Index (trend) + Douyin Featured (negative material) + WebSearch (web-wide news), and output a due-diligence sentiment report (negative-risk list / positive highlights / sentiment overview / overall judgment).
L1: Overview
Purpose: answer the core due-diligence questions — any negative publicity? litigation/penalty coverage? product-quality issues? executive misconduct? overall reputation?
Prerequisites: Kimi WebBridge daemon running (127.0.0.1:10086) + browser extension connected
L2: Procedure
Source 1: Douyin Index (heat trend)
Access Method
URL: https://trendinsight.oceanengine.com/arithmetic-index/analysis/keyword?keyword={company-short-name}&appName=aweme
Method: Kimi WebBridge opens the URL and extracts page content
Note: URL-parameterized access avoids captchas. Do not type into the search box.
Keyword Strategy
Dimension
Keyword
Overall heat
{short name}
Negative heat
{short name} 负面
Complaint heat
{short name} 投诉
Interpretation
Sudden spike (MoM > 200%) → a negative event may be fermenting; investigate
Flat trend → low reputation risk
Persistently high → normal for famous companies; judge by content
Source 2: Douyin Featured (negative material)
Access Method
URL: https://www.douyin.com/search/{company-short-name}?type=video
Method: Kimi WebBridge searches Douyin, extracts title / author / likes / comments
Note: search by short name (full names rarely hit); prioritize high-engagement videos.
Collected Fields
Collected fields: video title (negative signals: complaint / rights-protection / exposure / quality / scam / runaway), likes & comments (reach = impact), author (personal vent vs media report vs competitor smear), publish date (recent vs historical).
Signal Grading
Signals
Risk
投诉 / 维权 / 欺骗 / 诈骗 / 跑路
🔴 high
质量 / 召回 / 不合格 / 假货
🔴 high
欠薪 / 辞退 / 仲裁
🟡 medium
吐槽 / 差评 / 不满
🟡 medium
positive / awards / innovation
🟢 positive
Source 3: WebSearch (web-wide)
Search Keyword Groups
# High priority (always)search_queries_high=["{company} 负面 OR 投诉 OR 维权 OR 欺骗 OR 跑路","{company} 起诉 OR 法院 OR 被告 OR 被执行 OR 失信","{company} 处罚 OR 违规 OR 罚款 OR 整改 OR 通报批评",]# Medium priority (recommended)search_queries_mid=["{company} 质量 OR 召回 OR 不合格 OR 安全事故","{company} 老板 OR 法人 OR 总经理 丑闻 OR 被查 OR 被抓","{company} 欠薪 OR 辞退 OR 劳动仲裁 OR 工伤",]# Low priority (positive control)search_queries_low=["{company} 获奖 OR 创新 OR 认定 OR 排名",]
Collected Fields
Collected fields: title / source site / date / summary / URL. Note: distinguish the company as plaintiff (enforcement, not a negative signal) vs defendant (negative signal).
Step 1: Run the three channels
WebSearch first (highest information density): run high/medium/low query groups and collect results
Douyin Index: check the heat trend for signs of recent fermentation
Douyin Featured: search videos and filter by negative signal words
Step 2: LLM post-processing
Step
Description
Sentiment classification
tag each item positive / neutral / negative
Risk grading
negative → high (defendant / penalty / runaway / safety accident) / medium (complaints / quality gripes / labor) / low (generic bad reviews)
Event merging
merge duplicate coverage of one event (keep the earliest)
Plaintiff/defendant split
litigation where the company is plaintiff is not a negative risk
Authenticity: all content must come from actual collection (WebSearch results / Douyin pages); never fabricate
Traceability: every negative risk carries a source_url
Plaintiff/defendant: litigation where the company is plaintiff goes to "litigation updates", not negative risks
Recency: every negative item is marked recent (≤3 months) or historical
No absolute conclusions: sentiment is one auxiliary signal; the report must note it should be combined with business-registration, judicial, and financial data
Error Handling
Scenario
Handling
Douyin Index unreachable
skip; supplement with WebSearch + Featured; note "heat data missing" in the report
Douyin search requires login
skip; WebSearch {company} 抖音 负面 instead
WebSearch returns nothing
mark "no negative found" — a positive signal
Too many negatives
keep the top 10 by severity; count the rest
Known Limitations
Not real-time: depends on search-engine indexing and Douyin pages; hour-to-day latency (7×24 monitoring requires commercial sentiment APIs)
LLM-based sentiment: no dedicated sentiment model; ~85-90% accuracy, edge cases may misjudge
Douyin coverage: WeChat/Weibo/Xiaohongshu content is only covered indirectly via WebSearch
Full vs short name: WebSearch uses the full name (precision), Douyin the short name (recall); results may differ
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