# Twitter Post Comments (`monumental_world/twitter-comments`) Actor Get Twitter post comments. - **URL**: https://apify.com/monumental\_world/twitter-comments.md - **Developed by:** [Raised Pro](https://apify.com/monumental_world) (community) - **Categories:** Social media - **Stats:** 80 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 "# Twitter (X.com) Post Comments Export to Excel - No Login Needed ### Need to download Twitter (X.com) comment data to Excel? This tool helps you extract replies and engagement metrics instantly. Whether you're a social media manager, agency owner, or business analyst, this tool makes it easy to export Twitter post comments directly into Excel or CSV format. No technical skills required, no login neededβ€”just paste a tweet URL and download your data in seconds. Perfect for agencies managing multiple clients, marketers tracking campaign performance, and anyone who needs to analyze Twitter engagement without the hassle. ### Step-by-Step Guide **Step 1: Enter the Tweet ID** Find the Twitter post you want to analyze and copy its Tweet ID. This is the long number at the end of the tweet URL. For example, in `https://twitter.com/user/status/1738106896777699464`, the Tweet ID is `1738106896777699464`. **Step 2: Click ""Start"" (No login required)** Paste the Tweet ID into the tool and hit start. The tool will automatically begin collecting all comments and replies from that post. You don't need to sign in to Twitter or provide any credentials. **Step 3: Download your data as Excel, CSV, or XML** Once the extraction is complete, download your file in your preferred format. Open it in Excel, Google Sheets, or any spreadsheet program to start analyzing your data immediately. ### What columns will I get? Your download will include the following columns: - **Comment ID** - Unique identifier for each comment - **Post ID** - The original tweet's identifier - **Comment Text** - The full text of each reply or comment - **Comment Date** - When the comment was posted - **Comment Likes Count** - Number of likes the comment received - **Author Username** - The commenter's Twitter handle - **Author Display Name** - The commenter's profile name - **Author Verified** - Whether the commenter has a verified badge - **Reply to Comment ID** - If this is a reply to another comment, its ID - **Comment Edited** - Whether the comment has been edited - **Comment Reported Count** - Number of times the comment was reported - **Comment Sentiment Score** - Automated sentiment analysis (0-1 scale, higher is more positive) ### How to use this data **Analyze audience sentiment and feedback** Use the sentiment score column to quickly identify positive, negative, and neutral comments. Filter by sentiment to understand how your audience truly feels about your content and make data-driven decisions for future posts. **Track engagement trends over time** Export comments from multiple posts and compare engagement metrics like reply counts, likes per comment, and response times. Identify which content types generate the most meaningful conversations with your audience. **Create comprehensive client reports** Add the exported data to your monthly reports to show clients exactly how their Twitter content is performing. Include top comments, engagement rates, and sentiment analysis to demonstrate the value of your social media strategy. ### Input Settings **Tweet ID** This is the unique number that identifies a specific tweet on Twitter. You can find it at the end of any tweet URL. Simply copy the long number after `/status/` in the web address and paste it into the tool. For example: `1738106896777699464` ### Frequently Asked Questions **Q: Do I need a Twitter (X.com) account?** No! This tool works without requiring you to log in or have a Twitter account. Just provide the Tweet ID and you're ready to go. **Q: Can I open this in Google Sheets?** Yes, absolutely! Download the CSV format and upload it directly to Google Sheets. You can also open Excel files in Google Sheets by uploading them to Google Drive. **Q: How many comments can I export?** The tool will extract all available comments from the specified tweet, giving you a complete dataset for your analysis. **Q: Is the sentiment score accurate?** The sentiment score is generated using automated analysis and provides a helpful starting point. For critical decisions, we recommend reviewing the actual comment text alongside the score. **Q: Can I export comments from private accounts?** The tool can only access publicly available comments. Replies from private or protected accounts won't be included in your export. --- *Keywords: post comments scraper, social media comments extractor, instagram post comments scraping, facebook post comments scraper, export comments from posts, comment data scraping tool, social engagement analytics*" # Actor input Schema ## `tweetId` (type: `string`): Tweet ID ## `maxPages` (type: `integer`): Maximum number of pages to fetch (pagination handled automatically) ## Actor input object example ```json { "tweetId": "2024799518181700017", "maxPages": 1 } ```` # 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("monumental_world/twitter-comments").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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("monumental_world/twitter-comments").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call monumental_world/twitter-comments --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=monumental_world/twitter-comments", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter Post Comments", "description": "Get Twitter post comments.", "version": "1.0", "x-build-id": "SNIoSVR4eonDwVgwP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/monumental_world~twitter-comments/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-monumental_world-twitter-comments", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/monumental_world~twitter-comments/runs": { "post": { "operationId": "runs-sync-monumental_world-twitter-comments", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/monumental_world~twitter-comments/run-sync": { "post": { "operationId": "run-sync-monumental_world-twitter-comments", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "tweetId" ], "properties": { "tweetId": { "title": "Tweet ID", "type": "string", "description": "Tweet ID", "default": "2024799518181700017" }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages to fetch (pagination handled automatically)", "default": 1 } } }, "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 } } } } } } } } } } ```