# Linkedin Search Comments Spider (`getdataforme/linkedin-search-comments-spider`) Actor Extract LinkedIn comments using keyword search with this powerful Apify Actor. Collect comment text, author details, profile URLs, timestamps, and activity data using valid LinkedIn session cookies (JsessionId and LiAt). Perfect for lead generation, research, and social listening. - **URL**: https://apify.com/getdataforme/linkedin-search-comments-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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.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 ## Linkedin Search Comments Spider Extract LinkedIn post comments based on keyword search results. This Apify Actor helps you discover public engagement around specific topics by collecting comments, authors, timestamps, and LinkedIn activity data. Perfect for lead generation, audience research, recruitment analysis, social listening, and engagement monitoring on LinkedIn. --- ### Features * 🔍 Search LinkedIn comments using custom keywords * 💬 Extract full comment text and LinkedIn activity details * 👤 Collect author profile information and LinkedIn profile URLs * ⏱ Retrieve timestamps and edited status * 🚀 Fast and scalable Apify Actor ready for automation --- ## Input Example Users need to provide valid LinkedIn session cookies along with a search query. ```json { "JsessionId": "ajax:36fdfdf889fdf41662", "LiAt": "AQEDAU8D6qUBxwAQAd3UIIdH7PkV3c1u_RpDjEbS_YlfdfdfKw", "Query": "content writing" } ```` *** ## Input Parameters | Parameter | Type | Required | Description | | ------------ | ------ | -------- | ------------------------------------------- | | `JsessionId` | String | Yes | Your LinkedIn `JSESSIONID` session cookie | | `LiAt` | String | Yes | Your LinkedIn `li_at` authentication cookie | | `Query` | String | Yes | Keyword used to search LinkedIn comments | *** ## Output Example ```json [ [ { "activity": "7458051535084863488", "comment_urn": "urn:li:comment:(activity:7458051535084863488,7458157127652769792)", "comment_text": "Cfbr", "author_name": "Devanshu Srivastava", "author_url": "https://www.linkedin.com/in/devsrivastava3", "author_id": "ACoAADri_h4BEuDd_IoazBxmHjmG2gL2IslX9kI", "created_at": 1778163225092, "edited": false, "actor_id": "CCUcBDrMcBoLXwFSF", "run_id": "xnwF2QkAa3qbBNZHh" }, { "activity": "7458109890406912000", "comment_urn": "urn:li:comment:(activity:7458109890406912000,7458306544397377536)", "comment_text": "Hi Rahul,\nI'm here to write SEO content, turn ideas into words. Let's connect.", "author_name": "Mir Naeem Talpur", "author_url": "https://www.linkedin.com/in/mir-naeem-talpur-1a7056278", "author_id": "ACoAAEOjfo8BI3FpE_LT4s5PNxKZ8YPo0vjNA-w", "created_at": 1778198848819, "edited": false, "actor_id": "CCUcBDrMcBoLXwFSF", "run_id": "xnwF2QkAa3qbBNZHh" }, { "activity": "7458109890406912000", "comment_urn": "urn:li:comment:(activity:7458109890406912000,7458261488055549952)", "comment_text": "Let's connect Rahul.\nI am very much interested in this role and I am confident that I am the best suiter for this role.", "author_name": "Maryam Dilawar", "author_url": "https://www.linkedin.com/in/maryamaiwriter", "author_id": "ACoAAF2rnYQBJlDtZxswR9F22t2hdovtCeyT2v4", "created_at": 1778188106551, "edited": true, "actor_id": "CCUcBDrMcBoLXwFSF", "run_id": "xnwF2QkAa3qbBNZHh" } ] ] ``` *** ## Use Cases This Actor can be useful for: - Lead generation from LinkedIn discussions - Market and audience research - Recruitment and hiring outreach - Tracking engagement around industry topics - Social listening and competitor analysis - Discovering active LinkedIn users in specific niches *** ## How It Works 1. Provide your LinkedIn session cookies (`JsessionId` and `LiAt`) 2. Enter your target keyword in the `Query` 3. Run the Actor 4. Receive structured LinkedIn comments data in JSON format *** ### How to Get `JsessionId` and `LiAt` To use this Actor, you need valid LinkedIn session cookies: - `JsessionId` - `LiAt` The easiest way to extract them is by using the Cookie-Editor browser extension. *** #### Step 1: Install Cookie-Editor Extension Install the Cookie-Editor extension for your browser: - [Cookie-Editor Chrome Extension](https://chromewebstore.google.com/detail/cookie-editor/hlkenndednhfkekhgcdicdfddnkalmdm?utm_source=chatgpt.com) *** #### Step 2: Login to LinkedIn Open [LinkedIn](https://www.linkedin.com?utm_source=chatgpt.com) and log into your account. *** #### Step 3: Open Cookie-Editor Click the Cookie-Editor extension icon in your browser toolbar. You may see a permission popup like this: ![Cookie Editor Permission Popup](https://i.ibb.co/ksfQnDR5/cookieeditor.png) Click: - **This site** This grants the extension permission to read cookies for LinkedIn. *** #### Step 4: Copy Required Cookies After granting access, search for these cookies inside Cookie-Editor: - `JSESSIONID` - `li_at` Copy their values and use them in the Actor input. *** #### Example Input ```json id="x1sdbv" { "JsessionId": "ajax:36fdfdf889fdf41662", "LiAt": "AQEDAU8D6qUBxwAQAd3UIIdH7PkV3c1u_RpDjEbS_YlfdfdfKw", "Query": "content writing" } ``` *** #### Important Notes - Use fresh cookies for the best results - Do not share your LinkedIn cookies publicly - Expired cookies may cause authorization errors ## Important Notes - A valid LinkedIn authenticated session is required - Use fresh cookies to avoid authorization issues - Excessive requests may temporarily limit LinkedIn access *** ### Support For more custom/simplified outputs or bug reports, please contact: - - Or contact us here: [GetDataForMe Contact Page](https://getdataforme.com/contact/?utm_source=chatgpt.com) Please use the email subject: ```text Custom Support - Linkedin Search Comments Spider ``` Thank you! # Actor input Schema ## `Query` (type: `string`): The query for the spider. ## `LiAt` (type: `string`): The li at for the spider. ## `JsessionId` (type: `string`): The jsession id for the spider. ## Actor input object example ```json { "Query": "content writing" } ``` # 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("getdataforme/linkedin-search-comments-spider").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("getdataforme/linkedin-search-comments-spider").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 getdataforme/linkedin-search-comments-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/linkedin-search-comments-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Search Comments Spider", "description": "Extract LinkedIn comments using keyword search with this powerful Apify Actor. Collect comment text, author details, profile URLs, timestamps, and activity data using valid LinkedIn session cookies (JsessionId and LiAt). Perfect for lead generation, research, and social listening.", "version": "0.0", "x-build-id": "dndoW8ehzqu8wxOay" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~linkedin-search-comments-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-linkedin-search-comments-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~linkedin-search-comments-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-linkedin-search-comments-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~linkedin-search-comments-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-linkedin-search-comments-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Query": { "title": "Query", "type": "string", "description": "The query for the spider.", "default": "content writing" }, "LiAt": { "title": "Li At", "type": "string", "description": "The li at for the spider." }, "JsessionId": { "title": "Jsession Id", "type": "string", "description": "The jsession id for the spider." } } }, "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 } } } } } } } } } } ```