YouTube Video Comment Sentiment Signals

Get complete source-linked public comments with transparent lexical sentiment evidence for brand research teams, creator agencies, community analysts, and competitive-intelligence workflows. The Actor uses bounded public YouTube sources without login.

Buyer question

What transparent positive, negative, and neutral lexical signals appear in these public comments?

Start in 30 seconds

{
  "videoUrlsOrIds": [
    "dQw4w9WgXcQ"
  ],
  "maxCommentsPerVideo": 20,
  "maxResults": 20
}

Open the workflow-specific starter Task, run the bounded sample, inspect the Dataset and free RUN_SUMMARY, then replace the sample input.

Choose this Actor when

Choose this specialist when you need complete source-linked public comments with transparent lexical sentiment evidence with explicit evidence and billing boundaries. It is intentionally narrower than a generic YouTube scraper. Start with one query, video, channel, or playlist before scheduling a larger workflow.

First run checklist

1. Run the included bounded public example.
2. Verify source URLs, IDs, text, and evidence fields in the Dataset.
3. Compare paid rows with the free RUN_SUMMARY.
4. Save a Task, schedule, webhook, API, MCP, or AI-agent workflow only after the sample is useful.

Evidence and billing

Launch PPE price: $25.00 per 1,000 complete source-linked public comments with transparent lexical sentiment evidence, or $2.50 for 100. Diagnostics, failed or incomplete fetches, duplicates, empty searches, unsupported classifications, and truncated records are never billed.

Use a disclosed deterministic lexical score over the public comment text. Label ties as neutral and never infer emotion, identity, demographics, purchase intent, or author characteristics.

Output schema

Billable Dataset fields: videoId, videoUrl, commentId, commentUrl, authorName, commentText, sentiment, sentimentScore, positiveTerms, negativeTerms, sentimentMethod, evidenceDirect, scrapedAt. The separate RUN_SUMMARY reports requested inputs, saved rows, free failures, and source boundaries. Error-only or placeholder rows never enter the Dataset.

Source boundaries

The Actor uses public YouTube search, comment, channel metadata, or playlist-feed surfaces as required by this exact workflow. Cloud runs use Apify's plan-included rotating datacenter proxy because YouTube can require sign-in from shared Actor IPs. It never uses a residential proxy and does not return captions, transcripts, subtitles, private videos, members-only content, logged-in data, or inferred audience demographics.

Automation and AI agents

Use Apify Tasks, schedules, API, webhooks, ChatGPT, Claude, Neuton Actors MCP, n8n, Make, Zapier, Sheets, a warehouse, RAG pipeline, or agent workflow.

Hosted Apify MCP endpoint: https://mcp.apify.com/?tools=neuton/youtube-video-comment-sentiment-signals

Explore the YouTube data family

• YouTube Video Metadata Scraper for complete raw video fields
• YouTube Description Links Intelligence for explicit outbound links
• Neuton Actors MCP for AI-agent workflows

SEO keywords

YouTube comment sentiment, creator audience signals, lexical sentiment evidence, video community analysis, creator intelligence, Apify YouTube Actor.

Responsible use

Use public data for legitimate creator, media, marketing, research, education, moderation, and automation workflows. Do not bypass access controls, collect private content, identify anonymous people, infer sensitive traits, or make consequential decisions from lexical sentiment. Review YouTube's current terms and applicable privacy, advertising, and copyright rules before production use.