YouTube Claude Mentions Tracker

What does YouTube Claude Mentions do?

YouTube Claude Mentions is a focused scraper that finds every YouTube video talking about Claude Code, Claude AI, claude.ai, or Anthropic Claude, and ships a ranked dataset you can use for brand monitoring, competitor analysis, or sourcing tutorial creators. For each match it returns the video title, channel name, view count, publish date, the exact sentence that mentions Claude, a positive/neutral/negative sentiment label, the canonical YouTube URL, and the thumbnail URL. Source: youtube.com.

Run it once or schedule it weekly on the Apify platform for hands-off competitor intelligence — Apify handles proxy rotation, scheduling, API access, webhook integrations, and run monitoring out of the box.

Why use YouTube Claude Mentions?

• Brand & competitive intelligence — track how the AI community talks about Claude vs ChatGPT, Gemini, Copilot, or Cursor.
• Sentiment trendlines — diff weekly runs to see whether buzz is turning positive or negative after a model release.
• Find creator partners — surface high-view channels already producing Claude tutorials so you can sponsor, collaborate, or cite them.
• Tutorial discovery — collect the best Claude Code walkthroughs in one dataset instead of clicking through 100 YouTube search results.
• Sales triggers — spot creators reviewing competitor tools that haven't yet covered Claude.

How to use YouTube Claude Mentions

1. Click Start on this Actor's page on Apify Store.
2. Optionally edit the input on the Input tab — searchQueries, maxVideosPerQuery, since. The defaults work out of the box.
3. Press Save & Start. A typical run finishes in 2–5 minutes depending on transcript availability.
4. Open the Output tab to browse, sort, or download results as JSON, CSV, Excel, or HTML.
5. Hook into the Apify API, Zapier, Make, or your own webhook to pipe results into a dashboard.

Input

Field	Type	Default	Description
searchQueries	string[]	["Claude Code tutorial", "Claude AI coding", "Anthropic Claude review"]	YouTube search queries to scan, sorted by upload date so freshest videos come first.
maxVideosPerQuery	integer	30	Upper bound on videos inspected per query before transcript fetching.
since	ISO date string	90 days ago	Skip videos older than this date (uses YouTube's relative time text).

Example input JSON:

{
  "searchQueries": [
    "Claude Code tutorial",
    "Anthropic Claude vs ChatGPT",
    "claude.ai review 2026"
  ],
  "maxVideosPerQuery": 50,
  "since": "2026-03-01"
}

Output

Each dataset record represents one video with a verified Claude mention:

{
  "title": "I built a SaaS in 3 hours using Claude Code",
  "channelName": "AI Builder",
  "channelUrl": "https://www.youtube.com/@AIBuilder",
  "views": 142000,
  "publishDate": "2026-04-18T00:00:00.000Z",
  "publishedText": "1 month ago",
  "mentionContext": "Claude Code is amazing — it handles the whole repo at once and the diffs are clean.",
  "sentiment": "positive",
  "sentimentScore": 2,
  "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "thumbnailUrl": "https://i.ytimg.com/vi/dQw4w9WgXcQ/hqdefault.jpg",
  "videoId": "dQw4w9WgXcQ",
  "durationSeconds": 832,
  "descriptionSnippet": "In this video I show how Claude Code shipped a working app in under three hours.",
  "transcriptAvailable": true,
  "matchedQuery": "Claude Code tutorial",
  "scrapedAt": "2026-05-21T18:42:11.234Z"
}

You can download the dataset in JSON, HTML, CSV, or Excel from the Output tab, or fetch it programmatically via the Apify Dataset API.

Data table

Field	Description
title	Video title
channelName	Channel display name
channelUrl	Channel URL
views	Approximate view count at scrape time
publishDate	Best-effort ISO publish date parsed from "X days ago"
publishedText	Raw relative time text from YouTube
mentionContext	The ~1-sentence excerpt around the Claude mention
sentiment	positive, neutral, or negative (keyword heuristic)
sentimentScore	Integer score (positive words minus negative words)
url	Canonical video URL
thumbnailUrl	Highest-resolution thumbnail URL
videoId	YouTube video ID
durationSeconds	Video length
descriptionSnippet	First metadata snippet shown on search results
transcriptAvailable	Whether the auto-generated transcript was fetched
matchedQuery	Which input query surfaced this video
scrapedAt	ISO timestamp of the scrape

How much does it cost to scrape YouTube mentions?

This Actor is pay-per-result style work — most runs finish in 2–5 minutes of compute. With the Apify free tier you get $5/month of free platform credit, which typically covers tens of runs of this Actor at default settings. Heavy users (large maxVideosPerQuery, many queries) should expect <$0.10 per run on a paid plan. Set maxVideosPerQuery lower for faster, cheaper runs.

Tips and advanced options

• Schedule weekly. Set a weekly cron in the Apify Console and diff datasets to track sentiment over time.
• Add competitor keywords. Append queries like Cursor AI vs Claude, GPT-5 vs Claude, Copilot vs Claude Code to surface cross-product comparison videos.
• Use webhooks. Wire the Actor to Slack or Discord via Apify integrations so your team gets a ping on every new high-view mention.
• Combine with downstream Actors. Pipe mentionContext strings into a sentiment LLM for higher-accuracy classification, or feed channelUrl into a YouTube channel scraper for deeper enrichment.
• Tune since. A tight since window (e.g. last 7 days) gives a daily news-style feed; a wide one (12 months) gives a market overview.

FAQ, disclaimers, and support

Is this legal? This Actor scrapes only publicly available YouTube search results and the publicly accessible auto-generated transcripts that YouTube serves to anonymous users. Respect YouTube's Terms of Service and your local laws. Don't republish copyrighted transcripts wholesale.

Why are some videos missing a transcript? Not every video has captions, and YouTube periodically rotates its caption-track URL format. When transcripts are unavailable we fall back to matching against the title and description only.

How accurate is the sentiment label? It's a transparent keyword heuristic, ideal for trend-line tracking. For production-grade sentiment, pipe mentionContext into an LLM (Apify has official AI integrations).

Found a bug or want a feature? File an issue on the Apify Store Actor page or contact Ian Mu at github.com/ianymu.

Built by Ian Mu — solo founder shipping the Claude Verify-Before-Stop toolchain and a 100-actor Apify portfolio. Generated with Claude Code using Claude Opus 4.7.