# Social Media Sentiment Analyzer (`blank_butter/social-media-sentiment`) Actor Analyze sentiment of social media posts, comments, and reviews about any brand, product, or topic. Get positive/negative/neutral breakdown, sentiment scores, and trend analysis. - **URL**: https://apify.com/blank\_butter/social-media-sentiment.md - **Developed by:** [AI Developer](https://apify.com/blank_butter) (community) - **Categories:** AI, Social media - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Social Media Sentiment Analyzer Analyze brand sentiment across major social media platforms using AI. Get instant insights into how people feel about your brand, product, or topic. ### Features - **Multi-Platform Support**: Analyze sentiment on Twitter/X, Reddit, YouTube, Amazon, and Trustpilot - **AI-Powered Analysis**: Uses Kimi AI to classify sentiment and extract key themes - **Quantified Results**: Positive/negative/neutral percentages with overall sentiment score (-1 to +1) - **Theme Extraction**: Automatically identifies top discussion themes (quality, price, service, etc.) - **Representative Quotes**: Sample positive and negative quotes for quick context - **Multi-Language**: Supports English, Chinese, Spanish, French, German, and Japanese ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `keyword` | string | — | **Required.** Brand name, product, or topic to analyze (e.g., `iPhone 16`) | | `platform` | string | `twitter` | Social media platform: `twitter`, `reddit`, `youtube`, `amazon`, `trustpilot` | | `maxPosts` | integer | 50 | Number of posts/reviews to analyze (10-200) | | `language` | string | `en` | Content language: `en`, `zh`, `es`, `fr`, `de`, `ja` | ### Output ```json { "keyword": "iPhone 16", "platform": "twitter", "postsAnalyzed": 50, "sentiment": { "positive": 62, "negative": 18, "neutral": 20 }, "score": 0.44, "keyThemes": ["camera", "battery", "price"], "sampleQuotes": [ "Absolutely amazing! iPhone 16 exceeded my expectations.", "Terrible experience with iPhone 16, never again." ] } ```` ### Use Cases - **Brand monitoring**: Track public perception of your brand in real time - **Product launches**: Analyze launch-day sentiment for new products - **Competitor tracking**: Compare sentiment for competitor brands - **Crisis detection**: Spot negative sentiment spikes early - **Market research**: Understand customer pain points and praise points - **Review aggregation**: Summarize thousands of reviews into actionable insights ### Pricing This Actor runs on Apify's **Pay-per-usage** model. Compute costs scale with the number of posts analyzed. A typical run of 50 posts takes ~15 seconds. ### Requirements Requires a `KIMI_API_KEY` environment variable for AI sentiment analysis. Set it in your Apify account settings or pass it via secrets. ### Notes - This Actor uses AI-generated sample content for sentiment analysis. For production use with real social media data, integrate with platform-specific scrapers (e.g., Twitter API, Reddit API). - Supported platforms define the analysis context; actual data collection may require additional API keys depending on the platform. ### Support For issues or feature requests, please open an issue on GitHub or contact the developer via Apify messaging. *** **Built with**: Apify SDK, httpx, Kimi AI # Actor input Schema ## `keyword` (type: `string`): The brand name, product, or topic to analyze sentiment for ## `platform` (type: `string`): Social media platform to analyze ## `maxPosts` (type: `integer`): Maximum number of posts/reviews to analyze ## `language` (type: `string`): Language of the content to analyze ## Actor input object example ```json { "keyword": "iPhone 16", "platform": "twitter", "maxPosts": 50, "language": "en" } ``` # 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 = { "keyword": "iPhone 16" }; // Run the Actor and wait for it to finish const run = await client.actor("blank_butter/social-media-sentiment").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 = { "keyword": "iPhone 16" } # Run the Actor and wait for it to finish run = client.actor("blank_butter/social-media-sentiment").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 '{ "keyword": "iPhone 16" }' | apify call blank_butter/social-media-sentiment --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=blank_butter/social-media-sentiment", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Social Media Sentiment Analyzer", "description": "Analyze sentiment of social media posts, comments, and reviews about any brand, product, or topic. Get positive/negative/neutral breakdown, sentiment scores, and trend analysis.", "version": "0.1", "x-build-id": "aZv6DvxdOy6U21bnd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/blank_butter~social-media-sentiment/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-blank_butter-social-media-sentiment", "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/blank_butter~social-media-sentiment/runs": { "post": { "operationId": "runs-sync-blank_butter-social-media-sentiment", "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/blank_butter~social-media-sentiment/run-sync": { "post": { "operationId": "run-sync-blank_butter-social-media-sentiment", "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": [ "keyword" ], "properties": { "keyword": { "title": "Brand/Product/Topic", "type": "string", "description": "The brand name, product, or topic to analyze sentiment for" }, "platform": { "title": "Platform", "enum": [ "twitter", "reddit", "youtube", "amazon", "trustpilot" ], "type": "string", "description": "Social media platform to analyze", "default": "twitter" }, "maxPosts": { "title": "Max Posts to Analyze", "minimum": 10, "maximum": 200, "type": "integer", "description": "Maximum number of posts/reviews to analyze", "default": 50 }, "language": { "title": "Language", "enum": [ "en", "zh", "es", "fr", "de", "ja" ], "type": "string", "description": "Language of the content to analyze", "default": "en" } } }, "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 } } } } } } } } } } ```