extracting-youtube-comments-for-research
Extracts and analyzes YouTube comments for audience research using apidojo's YouTube scraper on Apify. Triggers when the user asks to: extract YouTube comments for research, analyze what viewers say in YouTube comments, scrape comments from a YouTube video for sentiment analysis, find common questions in YouTube comments, research audience feedback from YouTube video comments, extract top comments from a YouTube channel for audience insights, or analyze viewer reactions from YouTube comment sections. Returns comment text, likes on comment, reply count, commenter username, and timestamp. Ideal for content creators, brand researchers, product teams, and audience insight analysts.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill extracting-youtube-comments-for-research- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads extracting-youtube-comments-for-research automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Extracting YouTube Comments for Research
Pulls YouTube video comments for sentiment analysis, question mining, and product feedback. YouTube comments are more considered than TikTok — viewers invest more time before commenting.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | Optional | [] | YouTube URLs — channels, playlists, Shorts, search results |
youtubeHandles | array | Optional | [] | YouTube channel handles (e.g. @kurzgesagt) |
getTrending | boolean | Optional | false | Retrieve trending videos |
keywords | array | Optional | [] | Search keywords |
gl | string | Optional | us | Country code for results (e.g. US, GB) |
hl | string | Optional | en | Language code (e.g. en, de) |
uploadDate | string | Optional | all | Upload date filter: any, hour, today, week, month, year |
duration | string | Optional | all | Duration filter: any, short, long |
features | string | Optional | all | Feature filter: 4k, hd, live, cc, 3d, hdr, etc. |
sort | string | Optional | r | Sort order for search results |
maxItems | number | Optional | Unlimited | Maximum videos to return |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Scrape comments from target videos
- [ ] Step 2: Filter and clean dataset
- [ ] Step 3: Analyze by research goal
- [ ] Step 4: Extract top themes and insights
- [ ] Step 5: Deliver comment research report
Step 1: Scrape Comments
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~youtube-comments-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~youtube-comments-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~youtube-comments-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format json
APIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~youtube-comments-scraper"
Input:
{
"startUrls": [{"url": "[VIDEO_URL_1]"}, {"url": "[VIDEO_URL_2]"}],
"type": "comments",
"maxComments": 500
}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~youtube-comments-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"startUrls": [{"url": "[VIDEO_URL]"}], "type": "comments", "maxComments": 500}'
Step 2: Clean Dataset
- Remove comments < 8 words (usually emoji-only or "great video!")
- Remove self-promotional comments (contain external links)
- Remove creator's own replies (match
authorNameto channel name) - Apply
min_likes_on_commentfilter if set
Step 3: Analyze by Goal
Questions: Contains "?", "how do you", "what is", "can you" Pain points: "I struggle", "I can't", "problem is", "doesn't work" Product feedback: Product mentions + opinion signals Sentiment: Standard lexical classifier (positive/negative/neutral)
comment_importance = likeCount * 0.60 + replyCount * 10 * 0.40
Step 4: Edge Cases
- Comments disabled: Note; try different video from same channel
- Mostly non-English: Report language distribution; filter to English if needed
- Spam invasion: Filter where same username appears > 3 times
- Brigaded comment section: > 50% share coordinated theme → flag as BRIGADED
Output Format
# YouTube Comment Analysis
Videos: [N] | Comments analyzed: [N] | After filtering: [N] | Date: [DATE]
## Sentiment (if goal = sentiment)
Positive: [X%] | Negative: [X%] | Neutral: [X%]
## Top 10 Most-Liked Comments
| # | Comment (excerpt) | Likes | Replies |
|---|------------------|-------|---------|
## Key Themes
| Theme | Frequency | Avg Likes | Example |
|-------|-----------|-----------|---------|
## Most Asked Questions
1. "[question]" — [N] viewers
Troubleshooting
Few comments returned: YouTube limits access for some videos; try high-comment video from same channel.
Mostly surface-level praise: Use min_likes_on_comment = 5 to filter for substantive comments.
Research goal not present: Audience may not engage that way on YouTube; try Reddit or TikTok for this niche.
FAQ
What does the extracting-youtube-comments-for-research skill do?
Extracts and analyzes YouTube comments for audience research using apidojo's YouTube scraper on Apify. Triggers when the user asks to: extract YouTube comments for research, analyze what viewers say in YouTube comments, scrape comments from a YouTube video for sentiment analysis, find common questions in YouTube comments, research audience feedback from YouTube video comments, extract top comments from a YouTube channel for audience insights, or analyze viewer reactions from YouTube comment sections. Returns comment text, likes on comment, reply count, commenter username, and timestamp. Ideal for content creators, brand researchers, product teams, and audience insight analysts.
How do I install extracting-youtube-comments-for-research?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill extracting-youtube-comments-for-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/extracting-youtube-comments-for-research/ (personal) or .claude/skills/extracting-youtube-comments-for-research/ (project).
Which AI agents can use extracting-youtube-comments-for-research?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is extracting-youtube-comments-for-research safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains extracting-youtube-comments-for-research?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 805 recorded installs.
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