# YouTube Comments Scraper PRO (Keyword & Sentiment Filter) (`aetherlabx/youtube-comments-scraper-pro`) Actor Export YouTube comments to CSV/JSON. Filter by keywords, minimum length, and exclude spam. Built for B2B lead gen, sentiment analysis, and competitor research. - **URL**: https://apify.com/aetherlabx/youtube-comments-scraper-pro.md - **Developed by:** [Aether](https://apify.com/aetherlabx) (community) - **Categories:** Lead generation, SEO tools, Videos - **Stats:** 2 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $4.90 / 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 ## ๐Ÿš€ YouTube Comments Scraper PRO: The B2B Intent Engine **๐Ÿ›‘ Stop Dumping Garbage Data. Start Intercepting Competitor Churn on YouTube.** Ordinary YouTube scrapers charge you to download 100,000 useless comments like "Nice video!" or "First!". You then waste hoursโ€”and expensive AI tokensโ€”trying to find the 5 comments that actually matter to your sales team. **YouTube Comments Scraper PRO is different. We don't sell raw data; we deliver ready-to-close B2B leads.** Our scraper scans thousands of comments *in-memory* and **ONLY exports the highly-qualified leads** who are actively looking to switch software or complaining about a pain point you can solve. --- ### ๐Ÿ”ฅ Why We Are the #1 Tool for SaaS Founders & SDRs #### ๐ŸŽฏ 1. Intent Keyword Radar Input trigger words like `"alternative"`, `"expensive"`, `"bug"`, or `"pricing"`. If a comment doesnโ€™t contain your keywords, we instantly discard it. You only pay for the leads that match your exact intent funnel. #### ๐Ÿงต 2. Smart Reply Thread Tracking When someone complains about a competitor, the real gold is in the replies (e.g., *"Try Cal.com instead!"*). If a parent comment hits your Keyword Radar, our scraper automatically rips out the entire reply thread to give your SDR full context. #### ๐Ÿงน 3. Auto-Noise & Spam Filter Set a minimum character length to instantly block low-effort comments. We also automatically detect and drop comments containing promotional `http://` links to keep your CRM perfectly clean of spam bots. #### ๐Ÿ’ก 4. Zero Hidden Compute Costs Unlike other scrapers that drain your Compute Units by downloading endless pages of junk, **we absorb the heavy lifting.** Our engine filters *before* saving. You only pay for the actual, high-quality results we deliver to your dataset. --- ### ๐Ÿ› ๏ธ The 3-Step Interception Playbook 1. **Target**: Paste the URL of a competitor's review or tutorial video (e.g., `Calendly vs Hubspot 2026`). 2. **Filter**: Add your buying intent keywords (e.g., `expensive`, `issue`, `looking for`). 3. **Export & Close**: Download a clean CSV of frustrated users, import them into your outreach tool (or n8n), and send a highly personalized message offering your solution. **Stop scrolling. Start closing.** # Actor input Schema ## `startUrls` (type: `array`): Paste the links to the YouTube videos you want to extract intent from. ## `sortBy` (type: `string`): Select 'Newest First' to capture recent intent signals, or 'Top Comments' for historical sentiment. ## `maxComments` (type: `integer`): Maximum number of raw comments to scan before applying filters. ## `keywords` (type: `array`): Keep ONLY comments containing these keywords. Leave empty to extract all. (Max 20 keywords) ## `minLength` (type: `integer`): Discard short, low-value comments (e.g., 'Nice!', 'First'). ## `ignoreLinks` (type: `boolean`): Automatically drop comments containing http:// or https:// to avoid self-promo spam. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } ], "sortBy": "newest", "maxComments": 100, "keywords": [ "issue" ], "minLength": 20, "ignoreLinks": true } ```` # 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 = { "startUrls": [ { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } ], "maxComments": 100, "keywords": [ "issue" ], "minLength": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("aetherlabx/youtube-comments-scraper-pro").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "startUrls": [{ "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" }], "maxComments": 100, "keywords": ["issue"], "minLength": 20, } # Run the Actor and wait for it to finish run = client.actor("aetherlabx/youtube-comments-scraper-pro").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } ], "maxComments": 100, "keywords": [ "issue" ], "minLength": 20 }' | apify call aetherlabx/youtube-comments-scraper-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=aetherlabx/youtube-comments-scraper-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "YouTube Comments Scraper PRO (Keyword & Sentiment Filter)", "description": "Export YouTube comments to CSV/JSON. Filter by keywords, minimum length, and exclude spam. Built for B2B lead gen, sentiment analysis, and competitor research.", "version": "0.0", "x-build-id": "n7EF03tT03dEN5gQc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/aetherlabx~youtube-comments-scraper-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-aetherlabx-youtube-comments-scraper-pro", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/aetherlabx~youtube-comments-scraper-pro/runs": { "post": { "operationId": "runs-sync-aetherlabx-youtube-comments-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/aetherlabx~youtube-comments-scraper-pro/run-sync": { "post": { "operationId": "run-sync-aetherlabx-youtube-comments-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "YouTube Video URLs", "type": "array", "description": "Paste the links to the YouTube videos you want to extract intent from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "sortBy": { "title": "Sort Comments By", "enum": [ "newest", "top" ], "type": "string", "description": "Select 'Newest First' to capture recent intent signals, or 'Top Comments' for historical sentiment.", "default": "newest" }, "maxComments": { "title": "Max Comments per Video", "minimum": 1, "type": "integer", "description": "Maximum number of raw comments to scan before applying filters.", "default": 500 }, "keywords": { "title": "Keyword/Regex Radar (Optional)", "maxItems": 20, "type": "array", "description": "Keep ONLY comments containing these keywords. Leave empty to extract all. (Max 20 keywords)", "items": { "type": "string" } }, "minLength": { "title": "Noise Filter: Minimum Length", "minimum": 0, "type": "integer", "description": "Discard short, low-value comments (e.g., 'Nice!', 'First').", "default": 20 }, "ignoreLinks": { "title": "Spam Filter: Exclude Links", "type": "boolean", "description": "Automatically drop comments containing http:// or https:// to avoid self-promo spam.", "default": true } } }, "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 } } } } } } } } } } ```