extracting-youtube-comments-for-research

Original：🇺🇸 English

Translated

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.

7installs

Sourceapidojo-io/apidojo-skills

Added on2026-09-03

NPX Install

npx skill4agent add apidojo-io/apidojo-skills extracting-youtube-comments-for-research

SKILL.md Content

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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_TOKEN
```
environment 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):

bash

# 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_TOKEN
must be set in environment or
.env
file.

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:

bash

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
```
authorName
```
to channel name)
Apply
```
min_likes_on_comment
```
filter 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.