# Youtube Comments Ultra (`knowten/youtube-comments-ultra`) Actor Professional YouTube comment scraper for sentiment analysis, brand monitoring, competitive intelligence and audience research. Extract complete comment threads with exact precision or blazing-fast estimates. Detect creator hearts. No authentication needed. Pay only for comments extracted. - **URL**: https://apify.com/knowten/youtube-comments-ultra.md - **Developed by:** [Knowten](https://apify.com/knowten) (community) - **Categories:** Videos, Social media - **Stats:** 3 total users, 1 monthly users, 91.2% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $0.40 / 1,000 per comment extracteds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 🚀 YouTube Comment Ultra ### The Ultra-Fast YouTube Comments Scraper for Professional Data Analysis Extract YouTube comments at lightning speed with **exact precision** or **blazing-fast estimates**. YouTube Comment Ultra is engineered for researchers, marketers, and analysts who need comprehensive comment data without the complexity. **Get comment data in minutes. Not hours.** --- ### 🎯 What is YouTube Comment Ultra? YouTube Comment Ultra is a professional-grade **HTTP-based comment scraper** that extracts: ✅ All comments and replies from YouTube videos ✅ Comment text, author names, timestamps, and engagement metrics ✅ Creator heart indicators and engagement data ✅ Complete thread structures (top-level + nested replies) ✅ Total accurate comment counts ✅ Zero authentication required — no proxies, no API keys Built for **sentiment analysis**, **brand monitoring**, **competitive intelligence**, and **large-scale audience research**. --- ### 💰 Pricing: Simple & Transparent **$0.40 per 1,000 comments** ($0.0004 per comment extracted) - **Per Comment Pricing:** $0.0004 per comment you extract - **No Hidden Costs:** Pay only for comments you actually scrape - **Batch Friendly:** Extract 10,000 comments for just $4.00 - **Fair Pricing:** The more you analyze, the more value you get #### Pricing Examples | Comments Extracted | Cost | |----------|------| | 100 comments | $0.04 | | 1,000 comments | $0.40 | | 10,000 comments | $4.00 | | 100,000 comments | $40.00 | --- ### 📊 Why Choose YouTube Comment Ultra? | Feature | Benefit | |---------|---------| | 🔥 **Ultra-Fast Extraction** | Get results instantly with estimated mode or guaranteed accuracy with exact mode | | 💬 **Complete Thread Data** | Capture all comment replies and nested threads in single extraction | | 🎯 **Smart Filtering** | Extract only comments from specific date ranges with `publishedAfter` | | 📊 **Flexible Output** | Export to JSON, CSV, Excel, or XML — ready for your analysis pipeline | | 🔐 **No Authentication** | Works without login — no session management, no API key complexity | | 💰 **Cost Effective** | Pay once per run, extract unlimited comments from unlimited videos | | 🧠 **AI-Ready Output** | Structured JSON format perfect for NLP, sentiment analysis, and ML training | --- ### 🏃 Two Speed Modes: Pick Your Perfect Balance #### ⚡ **Estimated Mode** (Default) - **Speed:** Instant results (seconds) - **Coverage:** YouTube's visible page count (~1,200 comments average) - **Accuracy:** Fast approximation based on YouTube's interface - **Use Case:** Quick competitive scans, trend monitoring, large-scale sampling **Best for:** Brand monitoring, social listening, rapid competitive analysis ```json { "startUrls": [ { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } ], "maxComments": 500, "exactCommentCount": false, "commentsSortBy": "0" } ```` #### 🎯 **Exact Mode** (100% Accuracy) - **Speed:** Thorough traversal (minutes, depending on comment volume) - **Coverage:** **Every single comment** on the video - **Accuracy:** Guaranteed true total — walks all comment continuations - **Use Case:** Sentiment analysis, detailed audience research, academic studies **Best for:** Comprehensive sentiment analysis, detailed market research, complete audience profiles ```json { "startUrls": [ { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } ], "maxComments": 2000, "exactCommentCount": true, "commentsSortBy": "1", "publishedAfter": "90 days" } ``` *** ### 📋 Input Parameters Configure your extraction with simple, human-friendly parameters: | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | **startUrls** | array | One or more YouTube video URLs to scrape | `[]` | | **maxComments** | integer | Maximum comments to store per video (0 = unlimited) | `100` | | **exactCommentCount** | boolean | `true` = accurate total, `false` = fast estimate | `false` | | **commentsSortBy** | string | `"0"` = Top comments, `"1"` = Newest first | `"0"` | | **publishedAfter** | string | Only comments after this date (e.g., `"2026-01-01"` or `"30 days"`) | `null` | #### Supported URL Types ``` ✅ Regular Videos: https://www.youtube.com/watch?v=VIDEO_ID ✅ Playlist Items: https://www.youtube.com/watch?v=VIDEO_ID&t=30s ``` *** ### 💡 Use Case Strategies #### Strategy 1: Quick Brand Monitoring Monitor competitors and trending videos for brand mentions in seconds. ```json { "startUrls": [ "https://www.youtube.com/watch?v=TRENDING_VIDEO_1", "https://www.youtube.com/watch?v=TRENDING_VIDEO_2" ], "maxComments": 200, "exactCommentCount": false, "commentsSortBy": "0" } ``` **Time to completion:** < 30 seconds\ **Use case:** Rapid competitive intelligence, social listening, brand mention tracking *** #### Strategy 2: Deep Sentiment Analysis Extract complete comment datasets for comprehensive NLP and sentiment analysis. ```json { "startUrls": [ "https://www.youtube.com/watch?v=PRODUCT_REVIEW_VIDEO" ], "maxComments": 1000, "exactCommentCount": true, "commentsSortBy": "1", "publishedAfter": "2025-01-01" } ``` **Time to completion:** 2-5 minutes (depending on comment volume)\ **Use case:** Product feedback analysis, customer sentiment, opinion mining, NLP training data *** #### Strategy 3: Trending Content Detection Analyze newest comments to identify emerging trends and real-time sentiment. ```json { "startUrls": [ "https://www.youtube.com/watch?v=BREAKING_NEWS_VIDEO" ], "maxComments": 500, "exactCommentCount": true, "commentsSortBy": "1" } ``` **Time to completion:** 1-3 minutes\ **Use case:** Trend detection, real-time social listening, breaking news analysis *** #### Strategy 4: Academic & Research Collect complete, documented datasets for research, with exact totals and full thread structures. ```json { "startUrls": [ "https://www.youtube.com/watch?v=RESEARCH_VIDEO_1", "https://www.youtube.com/watch?v=RESEARCH_VIDEO_2" ], "maxComments": 0, "exactCommentCount": true, "commentsSortBy": "0", "publishedAfter": "2024-01-01" } ``` **Time to completion:** 5-10 minutes per video\ **Use case:** Academic research, discourse analysis, sociological studies, longitudinal audience tracking *** ### 📦 Output Format Clean, structured JSON with comprehensive comment data ready for immediate analysis: ```json { "cid": "UgxRn0_LUxzRP2MybPR4AaABAg", "replyToCid": null, "type": "comment", "comment": "This is the most amazing video I've ever seen!", "author": "@CreatorName", "authorIsChannelOwner": false, "hasCreatorHeart": true, "voteCount": 245, "replyCount": 12, "publishedTimeText": "2 weeks ago", "videoId": "dQw4w9WgXcQ", "pageUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "commentsCount": 1258, "title": "Video Title Here" } ``` #### Field Definitions | Field | Type | Description | |-------|------|-------------| | `cid` | string | Unique comment ID (YouTube's internal ID) | | `replyToCid` | string|null | Parent comment ID (null if top-level comment) | | `type` | string | `"comment"` or `"reply"` | | `comment` | string | Full comment text | | `author` | string | Comment author's display name | | `authorIsChannelOwner` | boolean | True if author owns the video's channel | | `hasCreatorHeart` | boolean | True if video creator liked this comment | | `voteCount` | integer | Number of likes on the comment | | `replyCount` | integer | Number of direct replies to this comment | | `publishedTimeText` | string | Relative timestamp (e.g., "2 weeks ago") | | `videoId` | string | YouTube video ID | | `pageUrl` | string | Direct link to the video | | `commentsCount` | integer | **Estimated** or **exact** total comments on video | | `title` | string | Video title | *** ### 🎯 Common Use Cases #### 📊 Sentiment Analysis Analyze customer opinions and product feedback at scale. - Extract top-voted comments for quick wins - Use exact mode for comprehensive opinion coverage - Filter by date to track sentiment changes over time #### 🔍 Competitive Intelligence Monitor competitor videos and audience reactions. - Track brand mentions across multiple competitor videos - Identify customer pain points from competitor feedback - Discover emerging market trends from comment discussions #### 📈 Market Research Understand audience preferences and market gaps. - Analyze discussion threads for customer needs - Identify feature requests in comment replies - Track sentiment shifts for new product launches #### 🧠 NLP & Machine Learning Prepare training datasets for AI models. - Extract diverse comment samples for training - Use exact mode to ensure balanced datasets - Get structured JSON output compatible with ML pipelines #### 🚀 Social Listening Monitor real-time trends and brand mentions. - Use estimated mode for fast sampling - Sort by newest to catch trending discussions - Filter by date range to focus on specific periods *** ### ✨ Key Features ✅ **Complete Thread Capture** — Get top-level comments AND all nested replies in one run\ ✅ **Creator Hearts Detected** — Identify which comments were liked by the creator\ ✅ **Smart Threading** — Structured reply data shows comment relationships\ ✅ **Date Filtering** — Extract only recent comments with `publishedAfter`\ ✅ **Dual-Mode Speed** — Choose fast estimates or guaranteed accuracy\ ✅ **Batch Processing** — Scrape multiple videos in a single run\ ✅ **Export Flexibility** — JSON, CSV, Excel, XML all supported\ ✅ **No API Keys** — Works out of the box, no YouTube API quota needed *** ### 📝 Example Input & Output #### Running the Scraper ```json { "startUrls": [ { "url": "https://www.youtube.com/watch?v=AVmi2ffgVzk" } ], "maxComments": 300, "exactCommentCount": true, "commentsSortBy": "0", "publishedAfter": "30 days" } ``` **Results:** - ✅ Extracts up to 300 comments - ✅ Shows true total (exact mode) - ✅ Sorts by top comments - ✅ Only from last 30 days *** ### 🚀 Quick Start (3 Steps) 1. **Paste URLs**\ Add one or more YouTube video URLs to your input 2. **Pick a Mode** - Fast estimate? → Set `exactCommentCount: false` - Complete accuracy? → Set `exactCommentCount: true` 3. **Start & Download**\ Click Start and download your comment dataset in any format (JSON, CSV, Excel, XML) *** ### ⚠️ Notes - **Estimated Mode:** Uses YouTube's visible page count, typically ~1,200 comments visible - **Exact Mode:** Traverses all comment continuations for guaranteed accuracy - **Rate Limiting:** No authentication required; YouTube's standard rate limits apply - **Reply Threads:** Replies are captured and linked via `replyToCid` field - **Large Extractions:** For 10,000+ comments, exact mode may take several minutes *** ### 🆘 Troubleshooting **Getting fewer comments than expected?** - In estimated mode, YouTube only shows ~1,200 comments by default - Switch to `exactCommentCount: true` for complete traversal - Some videos have comments disabled or limited **Empty dataset?** - Check that the video URL is public and not deleted - Verify the video has comments enabled - Try a well-known video (e.g., Rick Roll) to test **Want results faster?** - Use `exactCommentCount: false` for instant results - Reduce `maxComments` to lower numbers - Sort by `"0"` (top comments) instead of `"1"` (newest) *** ### 💼 Perfect For - **Agencies** running client sentiment analysis projects - **Brands** monitoring competitor reactions in real-time - **Researchers** gathering large comment datasets for studies - **Analysts** conducting social listening and trend detection - **Data Teams** preparing ML training datasets - **Content Creators** understanding audience sentiment *** **YouTube Comment Ultra** — Extract insights from your audience, today. 🎯 # Actor input Schema ## `startUrls` (type: `array`): Paste one or more direct YouTube video URLs. ## `maxComments` (type: `integer`): Limits how many comments are saved in the dataset. Use 0 to store every matching comment. ## `exactCommentCount` (type: `boolean`): Turn on for the exact total comment count. Leave off for faster runs with YouTube's visible estimate. ## `commentsSortBy` (type: `string`): Choose which comment order to scrape. ## `publishedAfter` (type: `string`): Only store comments published on or after this date. Use an absolute date like 2026-01-01 or a relative value like 30 days. ## Actor input object example ```json { "startUrls": [], "maxComments": 100, "exactCommentCount": false, "commentsSortBy": "0" } ``` # Actor output Schema ## `results` (type: `string`): Dataset items containing extracted YouTube comments and their exact `commentsCount` values. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("knowten/youtube-comments-ultra").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("knowten/youtube-comments-ultra").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call knowten/youtube-comments-ultra --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=knowten/youtube-comments-ultra", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Youtube Comments Ultra", "description": "Professional YouTube comment scraper for sentiment analysis, brand monitoring, competitive intelligence and audience research. Extract complete comment threads with exact precision or blazing-fast estimates. Detect creator hearts. No authentication needed. Pay only for comments extracted.", "version": "0.0", "x-build-id": "hRcedfLndSWNBI1r4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/knowten~youtube-comments-ultra/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-knowten-youtube-comments-ultra", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/knowten~youtube-comments-ultra/runs": { "post": { "operationId": "runs-sync-knowten-youtube-comments-ultra", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/knowten~youtube-comments-ultra/run-sync": { "post": { "operationId": "run-sync-knowten-youtube-comments-ultra", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "startUrls": { "title": "YouTube video URLs", "type": "array", "description": "Paste one or more direct YouTube video URLs.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxComments": { "title": "Maximum comments to store", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Limits how many comments are saved in the dataset. Use 0 to store every matching comment.", "default": 100 }, "exactCommentCount": { "title": "Exact count mode", "type": "boolean", "description": "Turn on for the exact total comment count. Leave off for faster runs with YouTube's visible estimate.", "default": false }, "commentsSortBy": { "title": "Comment sorting", "enum": [ "0", "1" ], "type": "string", "description": "Choose which comment order to scrape.", "default": "0" }, "publishedAfter": { "title": "Comments published after", "type": "string", "description": "Only store comments published on or after this date. Use an absolute date like 2026-01-01 or a relative value like 30 days." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```