# TikTok Comment Intent Leads (`webdata_labs/tiktok-comment-intent-leads`) Actor \[💵 $3.00 / 1K] Turn TikTok comments into ranked purchase-intent leads, product questions, complaints, recommendation requests, and competitor mentions. - **URL**: https://apify.com/webdata\_labs/tiktok-comment-intent-leads.md - **Developed by:** [WebData Labs](https://apify.com/webdata_labs) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## TikTok Comment Intent Leads - Buyer Intent & Product Questions ### ✅ What does TikTok Comment Intent Leads do? TikTok Comment Intent Leads turns public TikTok comments into a ranked list of people expressing purchase intent, asking product questions, requesting recommendations, reporting problems, or mentioning competitors. Add TikTok video URLs and receive workflow-ready rows instead of manually reading thousands of comments. The Actor collects public comments and applies a transparent deterministic classifier. Results can be downloaded through the Apify API, scheduled, exported, or connected to automation tools. ### 🔎 Why use TikTok Comment Intent Leads? - Find commenters asking where to buy, how much a product costs, or whether it ships. - Surface recurring product objections and complaints for customer research. - Detect competitor mentions under your own or industry videos. - Prioritize comments by intent, engagement, and explicit matched signals. - Keep provenance: every lead includes its source video and classification method. This is not a contact-data scraper and does not invent emails, locations, demographics, or private attributes. It ranks public comments for legitimate research and outreach workflows. ### ⚙️ How to use TikTok Comment Intent Leads 1. Open the Input tab. 2. Paste one or more public TikTok video URLs. 3. Optionally add product and competitor keywords. 4. Choose the number of comments and minimum lead score. 5. Click **Save & Start**, then export the ranked dataset. ### 📥 Input ```json { "postUrls": ["https://www.tiktok.com/@creator/video/123456789"], "commentsPerPost": 50, "maxScrolls": 30, "minimumScore": 30, "productKeywords": ["serum", "moisturizer"], "competitorKeywords": ["Brand A", "Brand B"] } ```` Lowering `minimumScore` increases coverage but also noise. Start with 30 for discovery and use 50 or higher for a shorter outreach queue. ### 📤 Output ```json { "leadScore": 79, "confidence": "high", "intentCategories": ["purchase_intent", "product_question"], "commentText": "Where can I buy this and does it ship to the UK?", "authorUsername": "sample_buyer", "authorProfileUrl": "https://www.tiktok.com/@sample_buyer", "videoUrl": "https://www.tiktok.com/@creator/video/123456789", "matchedSignals": ["purchase_intent:...", "product_question:..."], "classificationMethod": "deterministic_rules_v1" } ``` Download results as JSON, CSV, Excel, XML, or HTML. A separate run-summary row reports comments scanned, leads found, hit rate, and the upstream run ID. ### 📊 Data fields | Field | Meaning | | --------------------- | --------------------------------------------------------- | | `leadScore` | 1-100 priority score | | `confidence` | Low, medium, or high rule confidence | | `intentCategories` | Purchase, question, recommendation, complaint, competitor | | `matchedSignals` | Auditable reasons for the classification | | `authorUsername` | Public TikTok username | | `videoUrl` | Source TikTok video | | `likes`, `replyCount` | Public engagement signals | ### 💵 Pricing and cost estimation Pricing is per delivered intent-lead row, with comment collection included. Check the current price on the Pricing tab before a large run, and use a small comment limit first to estimate your niche's hit rate. ### 🧩 Tips and advanced options - Add brand-specific product words to improve relevance. - Add competitor names only when competitor-monitoring rows are actionable. - Process a few high-relevance videos before scaling across an entire profile. - Schedule the same inputs to monitor buyer questions over time. The Actor analyzes each run independently and does not maintain a persistent change history. ### ❓ FAQ, limits, and responsible use Classification is rule-based, not a claim about a person's identity or guaranteed buying behavior. Sarcasm, slang, and unsupported languages may be missed. Deleted, private, or restricted TikTok content cannot be processed. Use public data only for a lawful purpose, respect TikTok's terms, and comply with privacy and direct-marketing rules applicable to you. ### Support For bugs or missing patterns, use the Actor Issues tab and include the run URL plus a sanitized example comment. # Actor input Schema ## `datasetId` (type: `string`): Optional default dataset ID from a prior TikTok Comments Scraper run. This avoids Actor-to-Actor execution and enriches existing rows. ## `postUrls` (type: `array`): Public TikTok video URLs to scan for buyer intent and other actionable signals. ## `commentsPerPost` (type: `integer`): Maximum top-level comments to collect for each post. ## `maxScrolls` (type: `integer`): Safety limit for requesting additional TikTok comment pages. ## `minimumScore` (type: `integer`): Only output intent rows at or above this score. ## `maxLeads` (type: `integer`): Hard limit for ranked intent-lead output rows. ## `productKeywords` (type: `array`): Optional words that increase relevance when mentioned. ## `competitorKeywords` (type: `array`): Brand or product names to flag as competitor mentions. ## Actor input object example ```json { "postUrls": [ "https://www.tiktok.com/@vida.luz70/video/7504077256151600414" ], "commentsPerPost": 10, "maxScrolls": 10, "minimumScore": 30, "maxLeads": 1000, "productKeywords": [], "competitorKeywords": [] } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description # 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 = { "postUrls": [ "https://www.tiktok.com/@vida.luz70/video/7504077256151600414" ], "commentsPerPost": 10, "maxScrolls": 10, "minimumScore": 30 }; // Run the Actor and wait for it to finish const run = await client.actor("webdata_labs/tiktok-comment-intent-leads").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 = { "postUrls": ["https://www.tiktok.com/@vida.luz70/video/7504077256151600414"], "commentsPerPost": 10, "maxScrolls": 10, "minimumScore": 30, } # Run the Actor and wait for it to finish run = client.actor("webdata_labs/tiktok-comment-intent-leads").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 '{ "postUrls": [ "https://www.tiktok.com/@vida.luz70/video/7504077256151600414" ], "commentsPerPost": 10, "maxScrolls": 10, "minimumScore": 30 }' | apify call webdata_labs/tiktok-comment-intent-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=webdata_labs/tiktok-comment-intent-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok Comment Intent Leads", "description": "[💵 $3.00 / 1K] Turn TikTok comments into ranked purchase-intent leads, product questions, complaints, recommendation requests, and competitor mentions.", "version": "0.1", "x-build-id": "1pI9FL7oOmyT3J6L8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/webdata_labs~tiktok-comment-intent-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-webdata_labs-tiktok-comment-intent-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/webdata_labs~tiktok-comment-intent-leads/runs": { "post": { "operationId": "runs-sync-webdata_labs-tiktok-comment-intent-leads", "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/webdata_labs~tiktok-comment-intent-leads/run-sync": { "post": { "operationId": "run-sync-webdata_labs-tiktok-comment-intent-leads", "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": { "datasetId": { "title": "Existing comments dataset ID", "type": "string", "description": "Optional default dataset ID from a prior TikTok Comments Scraper run. This avoids Actor-to-Actor execution and enriches existing rows." }, "postUrls": { "title": "TikTok video URLs", "type": "array", "description": "Public TikTok video URLs to scan for buyer intent and other actionable signals.", "items": { "type": "string" } }, "commentsPerPost": { "title": "Comments per post", "minimum": 10, "maximum": 2000, "type": "integer", "description": "Maximum top-level comments to collect for each post.", "default": 50 }, "maxScrolls": { "title": "Maximum comment-page scrolls", "minimum": 1, "maximum": 300, "type": "integer", "description": "Safety limit for requesting additional TikTok comment pages.", "default": 30 }, "minimumScore": { "title": "Minimum lead score", "minimum": 1, "maximum": 100, "type": "integer", "description": "Only output intent rows at or above this score.", "default": 30 }, "maxLeads": { "title": "Maximum output leads", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Hard limit for ranked intent-lead output rows.", "default": 1000 }, "productKeywords": { "title": "Product keywords", "type": "array", "description": "Optional words that increase relevance when mentioned.", "default": [], "items": { "type": "string" } }, "competitorKeywords": { "title": "Competitor keywords", "type": "array", "description": "Brand or product names to flag as competitor mentions.", "default": [], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```