extracting-tiktok-comments-for-research

Q: How do I install extracting-tiktok-comments-for-research?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill extracting-tiktok-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-tiktok-comments-for-research/ (personal) or .claude/skills/extracting-tiktok-comments-for-research/ (project).

by apidojo-ioMarketing & SEO

Extracts and analyzes TikTok comments from any video or creator using apidojo's TikTok Comments scraper on Apify. Triggers when the user asks to: scrape TikTok comments from a video, analyze what viewers say about a TikTok post, extract comment data for sentiment analysis, find top comments on a viral TikTok video, collect TikTok user feedback from comments, build a dataset of TikTok community reactions, study audience sentiment on TikTok content, or research what a target audience cares about from TikTok comments. Returns commenter username, comment text, likes on comment, reply count, and timestamp. Ideal for market researchers, brand managers, content creators, and academic researchers.

Installs: 767
Last 24h: +8
Trending rank: #9617
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/apidojo-io/apidojo-skills --skill extracting-tiktok-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-tiktok-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 TikTok Comments for Research

Pulls all public comments from TikTok videos for audience sentiment analysis, product research, or competitive intelligence. Comments are the rawest form of consumer voice — unfiltered reactions at scale.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`startUrls`	array	✅	`[]`	TikTok video URLs to scrape comments from
`includeReplies`	boolean	Optional	`false`	Include reply comments (nested)
`maxItems`	number	Optional	Unlimited	Maximum comments to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Identify target video(s) and research goal
- [ ] Step 2: Run tiktok-comments-scraper
- [ ] Step 3: Fetch and clean comment dataset
- [ ] Step 4: Analyze themes, sentiment, and top comments
- [ ] Step 5: Deliver research output

Step 1: Clarify Parameters

Ask the user for:

TikTok video URL(s) — direct links to specific videos (e.g., https://www.tiktok.com/@creator/video/[ID]) OR
Creator handle — pull comments from their most recent/viral videos
Max comments per video (default: 500; max: ~3,000)
Research goal — sentiment analysis, product feedback, audience profiling, or competitive intel
Date filter (optional — focus on recent comments only)

Tip for best research: Use 3-5 videos from the same creator or about the same topic for a reliable dataset.

Step 2: Run the Actor

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-comments-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-comments-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-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~tiktok-comments-scraper"
Input:
{
  "postURLs": [
    "https://www.tiktok.com/@[handle]/video/[VIDEO_ID_1]",
    "https://www.tiktok.com/@[handle]/video/[VIDEO_ID_2]"
  ],
  "maxCommentsPerPost": 500,
  "includeReplies": false
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tiktok-comments-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "postURLs": [
      "https://www.tiktok.com/@[handle]/video/[VIDEO_ID]"
    ],
    "maxCommentsPerPost": 500,
    "includeReplies": false
  }'

Wait for SUCCEEDED. Fetch dataset.

Step 3: Clean Comment Dataset

From raw dataset, extract per comment:

text — the comment text
author.uniqueId — commenter username
diggCount — likes on the comment
replyCommentTotal — how many replies this comment received
createTime — timestamp

Clean:

Remove empty or emoji-only comments (if doing text analysis)
Remove spam patterns (repeated text, links, self-promotions)
Remove the creator's own replies (identified by matching author.uniqueId)

Step 4: Analyze by Goal

Goal: Sentiment analysis Classify each comment as Positive / Negative / Neutral (use the same lexical method as the Twitter sentiment skill). Weight by diggCount — a liked comment reflects community agreement.

Goal: Product feedback Look for:

Feature requests: "I wish", "you should", "would be better if", "needs"
Pain points: "why doesn't it", "can't believe", "problem with", "doesn't work"
Specific product mentions: nouns that repeat across multiple comments

Goal: Audience profiling From commenter bios (if available) and comment language:

Identify audience demographics signals (age signals, geographic signals, interest signals)
Find what questions the audience asks most

Goal: Top comments Simply sort by diggCount descending. Top-liked comments represent the community's most agreed-upon reactions.

Step 5: Format Output

Output Format

# TikTok Comment Analysis
Video(s): [N] | Total comments analyzed: [N] | Date: [DATE]

## Source Videos
| Video | Creator | Views | Comments Extracted |
|-------|---------|-------|-------------------|
| [url] | @[handle] | [N] | [N] |

## Sentiment Distribution (if goal = sentiment)
Positive: [X%] ([N] comments) | Negative: [X%] | Neutral: [X%]
Weighted by likes — Positive: [X%] | Negative: [X%]

## Top 10 Most-Liked Comments
| # | Comment | Likes | Replies |
|---|---------|-------|---------|
| 1 | "[comment text]" | [N] | [N] |

## Key Themes in Comments
| Theme | Frequency | Avg Likes per Comment |
|-------|-----------|----------------------|
| [Theme 1] | [N] | [N] |
| [Theme 2] | [N] | [N] |

## Most Asked Questions
1. "[question text]" — asked by [N] commenters
2. "[question text]" — [N] commenters

## Common Complaints / Pain Points
1. "[pain point]" — [N] comments, [N] total likes

## Audience Signals
- Age/demographic indicators: [summary]
- Geographic signals: [summary]
- Interest signals: [summary]

Troubleshooting

Few comments returned: Video may have comments disabled or be relatively new. Try a different video. All comments in non-English: Add a language filter post-processing, or adjust the search to English-language TikTok creators. Spam dominates results: Apply a filter: remove comments shorter than 5 words AND with 0 likes, which tend to be bots.

FAQ

What does the extracting-tiktok-comments-for-research skill do?

How do I install extracting-tiktok-comments-for-research?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill extracting-tiktok-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-tiktok-comments-for-research/ (personal) or .claude/skills/extracting-tiktok-comments-for-research/ (project).

Which AI agents can use extracting-tiktok-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-tiktok-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-tiktok-comments-for-research?

It is published by apidojo-io in apidojo-io/apidojo-skills. It has 767 recorded installs.

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extracting-tiktok-comments-for-research

Install

Install

Describe the task

Review the result

SKILL.md

Extracting TikTok Comments for Research

Prerequisites

Inputs

Workflow

Step 1: Clarify Parameters

Step 2: Run the Actor

Step 3: Clean Comment Dataset

Step 4: Analyze by Goal

Step 5: Format Output

Output Format

Troubleshooting

FAQ

More from apidojo-io/apidojo-skills

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