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
| 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
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_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:
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
# 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.