# YouTube Video Comments scraper (`powerai/youtube-video-comment-scraper`) Actor Extract comprehensive comments from any YouTube video with detailed metadata including author information, engagement metrics, and timestamps. Perfect for sentiment analysis, community engagement tracking, and content feedback analysis. - **URL**: https://apify.com/powerai/youtube-video-comment-scraper.md - **Developed by:** [PowerAI](https://apify.com/powerai) (community) - **Categories:** Social media, Videos - **Stats:** 28 total users, 0 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 1,000 results 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 Video Comments Scraper A powerful tool for extracting and analyzing comments from any YouTube video! This actor helps you gather comprehensive information about video comments, including author details, engagement metrics, and timestamps. ### ✨ Key Features - 💬 Extract comments from any public YouTube video - 👤 Get detailed comment metadata including: - Author information and badges - Comment content and timestamps - Engagement metrics (likes, replies) - Pinned status and creator hearts - 🔄 Support for comment replies - 📈 Support for large comment sections with automatic pagination - 🌐 Language and region customization - 📋 Clean and structured JSON output ### 💪 Why Choose This Actor? - **Comprehensive**: Get all comment details from any video - **Flexible**: Support for multiple comment parameters - **Efficient**: Automatic pagination for large comment sections - **Structured**: Clean JSON output for easy integration - **Reliable**: Stable and consistent data extraction - **User-Friendly**: Simple input parameters ### 🎯 Perfect For - 📊 Community Managers - 📈 Social Media Analysts - 🎥 Content Creators - 📱 Marketing Teams - 🔍 Sentiment Analysts - 📋 Customer Support Teams ### 📈 Use Cases 1. **Sentiment Analysis**: Analyze audience reactions and feedback 2. **Community Management**: Monitor and respond to comments 3. **Content Research**: Understand audience preferences 4. **Customer Support**: Track and address user issues 5. **Market Research**: Study audience engagement patterns ### 📝 Input Requirements The actor accepts the following input parameters: - `id` (required): The YouTube video ID - `maxResults` (optional): Maximum number of comments to extract (default: 20) - `hl` (optional): Language code for results (default: 'en') - `gl` (optional): Region code for results (default: 'US') - `cursor` (optional): Pagination cursor for next page of comments ### 📊 Output The actor returns a structured JSON response containing: - Comment details including: - Author information (name, avatar, badges) - Comment content and timestamps - Engagement metrics (likes, replies) - Pinned status and creator hearts - URLs (author channel) #### Input Example ```json { "id": "kJQP7kiw5Fk", "maxResults": 20, "hl": "en", "gl": "US" } ```` #### Output Example ```json [ { "commentId": "UgwW1S95-KHLW2bUI-14AaABAg", "content": "2017: People came to listen song. \n2025: People come to check views.", "author": { "channelId": "UC9Hq2JZT1aKlG8nJqd7CB-A", "title": "@Armaansingh-qv3qo", "isVerified": false, "isArtist": false, "isCreator": false, "avatar": [ { "url": "https://yt3.ggpht.com/noZToQvZxQysCE7i3Zs-a3O5iyHWXV_fPXke15wxaM_wFCoY9XbhWUW4pZMdhIwxrYJEmz1a=s88-c-k-c0x00ffffff-no-rj", "width": 88, "height": 88 } ], "avatarThumbnailUrl": "https://yt3.ggpht.com/noZToQvZxQysCE7i3Zs-a3O5iyHWXV_fPXke15wxaM_wFCoY9XbhWUW4pZMdhIwxrYJEmz1a=s88-c-k-c0x00ffffff-no-rj" }, "publishedTimeText": "4 years ago (edited)", "stats": { "votes": 726, "replies": 915 }, "cursorReplies": "Eg0SC2tKUVA3a2l3NUZrGAYygwEaUBIaVWd3VzFTOTUtS0hMVzJiVUktMTRBYUFCQWciAggAKhhVQ0xwOFJCaFFIdTl3U3NxNjJqX01kNkEyC2tKUVA3a2l3NUZrQABICoIBAggBQi9jb21tZW50LXJlcGxpZXMtaXRlbS1VZ3dXMVM5NS1LSExXMmJVSS0xNEFhQUJBZw%3D%3D" }, ... ] ``` # Actor input Schema ## `id` (type: `string`): The ID of the YouTube video to scrape comments from ## `maxResults` (type: `integer`): Maximum number of comments to extract ## `hl` (type: `string`): Language code for results (e.g., 'en' for English) ## `gl` (type: `string`): Region code for results (e.g., 'US' for United States) ## Actor input object example ```json { "id": "kJQP7kiw5Fk", "maxResults": 20, "hl": "en", "gl": "US" } ``` # 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("powerai/youtube-video-comment-scraper").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("powerai/youtube-video-comment-scraper").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 powerai/youtube-video-comment-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=powerai/youtube-video-comment-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "YouTube Video Comments scraper", "description": "Extract comprehensive comments from any YouTube video with detailed metadata including author information, engagement metrics, and timestamps. Perfect for sentiment analysis, community engagement tracking, and content feedback analysis.", "version": "0.0", "x-build-id": "hZGbULNNLyaustLcJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/powerai~youtube-video-comment-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-powerai-youtube-video-comment-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/powerai~youtube-video-comment-scraper/runs": { "post": { "operationId": "runs-sync-powerai-youtube-video-comment-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/powerai~youtube-video-comment-scraper/run-sync": { "post": { "operationId": "run-sync-powerai-youtube-video-comment-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "id" ], "properties": { "id": { "title": "Video ID", "type": "string", "description": "The ID of the YouTube video to scrape comments from", "default": "kJQP7kiw5Fk" }, "maxResults": { "title": "Maximum Results", "minimum": 10, "maximum": 10000, "type": "integer", "description": "Maximum number of comments to extract", "default": 20 }, "hl": { "title": "Language", "type": "string", "description": "Language code for results (e.g., 'en' for English)", "default": "en" }, "gl": { "title": "Region", "type": "string", "description": "Region code for results (e.g., 'US' for United States)", "default": "US" } } }, "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 } } } } } } } } } } ```