# ALL-IN-ONE market sentiment marker (`s4ndid/all-in-one-market-sentiment-marker`) Actor Type in a brand, get one number back. Scans Instagram, TikTok, YouTube and the news, classifies every voice, and returns a single Market Sentiment Index (-100 to +100) plus competitor benchmarks. Free news tier, no API key. - **URL**: https://apify.com/s4ndid/all-in-one-market-sentiment-marker.md - **Developed by:** [Sandy Yuanda](https://apify.com/s4ndid) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 100.0% 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## ALL-IN-ONE Market Sentiment Marker **Type in a brand. Get one number back.** This Actor listens to what the market is *actually* saying about any brand, product or company โ€” across **Instagram, TikTok, YouTube and the news** โ€” classifies every single voice as positive, negative or neutral, and boils it all down to a single **Market Sentiment Index** from **โˆ’100 to +100**. Then it does the same for your **competitors**, so you can see exactly where you stand. > ๐Ÿ†“ **Free to try.** The News channel costs nothing beyond the Actor's own compute โ€” no API key, no sub-Actors. Add social platforms when you want a fuller picture. --- ### ๐ŸŽฏ What you get ```` โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ MARKET SENTIMENT โ€” Nike index +6 (neutral) ยท 50 voices ๐Ÿ‘ 5 ๐Ÿ‘Ž 2 ๐Ÿ˜ 43 vs Adidas: -4 (neutral) vs Puma: +6 (neutral) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ ```` One headline score, backed by every voice it heard โ€” and you keep the receipts: each post, comment and headline is exported with its own sentiment label so you can audit exactly *why* the number is what it is. --- ### โœจ Why this one | | | |---|---| | ๐ŸŽฏ **One number, not a data dump** | The **Market Sentiment Index** (โˆ’100โ€ฆ+100) with a plain-English verdict: *very positive โ†’ very negative* | | ๐ŸฅŠ **Competitor benchmarking** | Score rivals the same way and compare side by side | | ๐Ÿ“ก **Four channels, one run** | Instagram, TikTok, YouTube, News โ€” combined into a single index | | ๐Ÿงพ **Fully auditable** | Every comment/headline is exported with its own label โ€” no black box | | ๐Ÿ†“ **Free tier that works** | News-only runs need no API key and call no paid Actors | | ๐Ÿ” **Deterministic by default** | The lexicon engine is free, instant and reproducible. LLM refine is opt-in with *your* key | --- ### โš™๏ธ Input Only **`brand`** is required. Everything else has sensible defaults. | Field | Type | Default | Description | |---|---|---|---| | `brand` | string | โ€” | **Required.** The brand/product/company to measure | | `keywords` | list | `[brand]` | Extra search terms (news, TikTok, YouTube) | | `hashtags` | list | brand | Hashtags for Instagram/TikTok discovery | | `platforms` | multi-select | `["news"]` | `news` (free), `instagram`, `tiktok`, `youtube` (paid) | | `competitors` | list | `[]` | Rivals to benchmark against | | `maxPostsPerPlatform` | integer | `10` | Posts/videos discovered per social platform | | `maxCommentsPerPost` | integer | `50` | Comments collected per post | | `maxNewsPerQuery` | integer | `50` | Headlines per keyword (free) | | `maxItems` | integer | `0` | Cap on dataset rows (`0` = unlimited) | | `includeRawItems` | boolean | `true` | Export every post/comment/headline, not just the scores | | `sentimentEngine` | select | `lexicon` | `lexicon` (free) or `llm` (your key) | | `openaiApiKey` | secret | โ€” | Only for `llm` โ€” used to re-check ambiguous items | | `proxy` | proxy | off | Optional proxy for the news fetches | #### Example ```json { "brand": "Nike", "keywords": ["Nike", "Nike Air Max"], "platforms": ["news", "youtube"], "competitors": ["Adidas", "Puma"], "maxPostsPerPlatform": 10, "maxCommentsPerPost": 50 } ```` *** ### ๐Ÿ“ค Output Row 1 is always **the marker**. Then the breakdowns, then (optionally) every raw voice. | `_kind` | What it is | |---|---| | `market_sentiment` | **The headline result** โ€” index, verdict, counts, per-platform + per-competitor breakdown | | `platform_sentiment` | Index and counts for one channel | | `competitor_sentiment` | Index and counts for one competitor | | `post` | A discovered social post/video | | `comment` | A comment, with its own `sentiment` + `score` | | `news` | A headline, with its own `sentiment` + `score` | ```json { "_kind": "market_sentiment", "brand": "Nike", "index": 6, "label": "neutral", "positive": 5, "negative": 2, "neutral": 43, "totalVoices": 50, "competitors": [ { "competitor": "Adidas", "index": -4, "label": "neutral", "total": 50 }, { "competitor": "Puma", "index": 6, "label": "neutral", "total": 50 } ] } ``` The same object is also written to the key-value store as **`SUMMARY`**. #### How the index is calculated ``` index = round( (positive โˆ’ negative) / total_voices ร— 100 ) ``` | Index | Verdict | |---|---| | โ‰ฅ +50 | very positive | | โ‰ฅ +15 | positive | | โˆ’15 โ€ฆ +15 | neutral | | โ‰ค โˆ’15 | negative | | โ‰ค โˆ’50 | very negative | Neutral voices stay in the denominator on purpose โ€” a brand with 5 fans, 2 critics and 43 people shrugging is **not** wildly positive, and the index says so. *** ### ๐Ÿ’ก Use cases - **Brand monitoring** โ€” track your index over time; schedule it daily. - **Competitive intelligence** โ€” benchmark against rivals in one run. - **Campaign / launch tracking** โ€” watch sentiment move around a release. - **Due diligence & market research** โ€” read the room before you commit. - **Crisis detection** โ€” schedule it and alert when the index drops. *** ### ๐Ÿ’ฐ Costs - **News** โ€” free. No API key, no sub-Actors. - **Instagram / TikTok / YouTube** โ€” these call established public Actors, billed to **your** account: `apify/instagram-hashtag-scraper` ยท `apify/instagram-comment-scraper` ยท `clockworks/tiktok-scraper` ยท `clockworks/tiktok-comments-scraper` ยท `streamers/youtube-scraper` ยท `streamers/youtube-comments-scraper` Cost scales with `maxPostsPerPlatform` ร— `maxCommentsPerPost`. Start small. *** ### ๐Ÿ“ Notes - The **lexicon** engine handles negation ("not bad"), multi-word phrases ("cash grab", "record high") and emoji (๐Ÿš€ ๐Ÿ“‰) โ€” it is deterministic and reproducible. - News headlines are factual by nature, so a healthy share land as **neutral**. That's honest signal, not a bug. - Enabling **LLM refine** only re-checks the items the lexicon called neutral, keeping token cost low. # Actor input Schema ## `brand` (type: `string`): The brand, product, company or topic you want to measure sentiment for. This is the only required field. ## `keywords` (type: `array`): Extra search terms used for news and for TikTok/YouTube search. Defaults to the brand name. Add product lines, slogans or misspellings to widen the net. ## `hashtags` (type: `array`): Hashtags used to discover Instagram and TikTok posts (without the `#`). Defaults to the brand name with spaces stripped. ## `platforms` (type: `array`): Which channels to listen to. **News is free.** Instagram, TikTok and YouTube each call a paid public Actor, billed to your account โ€” see the README for costs. ## `competitors` (type: `array`): Optional. Each competitor is scored the same way (via news) so you get a side-by-side sentiment comparison against your brand. ## `maxPostsPerPlatform` (type: `integer`): How many posts/videos to discover per social platform. Drives cost โ€” each post's comments are then scraped. ## `maxCommentsPerPost` (type: `integer`): How many comments to collect from each discovered post/video. ## `maxNewsPerQuery` (type: `integer`): How many news headlines to pull per keyword (and per competitor). Free. ## `maxItems` (type: `integer`): Hard cap on rows pushed to the dataset. `0` = no limit. The sentiment marker row is always written first. ## `includeRawItems` (type: `boolean`): Also output every individual post, comment and headline with its own sentiment label. Turn off if you only want the scores. ## `sentimentEngine` (type: `string`): **Lexicon** is fast, free and deterministic (no API key). **LLM** additionally re-checks the items the lexicon calls neutral, using your own OpenAI-compatible key. ## `openaiApiKey` (type: `string`): Only used when the engine is set to LLM. Your key is used to re-classify ambiguous items and is never stored. ## `llmModel` (type: `string`): Model name for the LLM refine step. ## `proxy` (type: `object`): Optional proxy for the direct (news) requests. Usually unnecessary. ## Actor input object example ```json { "brand": "Nike", "keywords": [ "Nike" ], "hashtags": [ "nike" ], "platforms": [ "news" ], "competitors": [], "maxPostsPerPlatform": 10, "maxCommentsPerPost": 50, "maxNewsPerQuery": 50, "maxItems": 0, "includeRawItems": true, "sentimentEngine": "lexicon", "llmModel": "gpt-4o-mini", "proxy": { "useApifyProxy": false } } ``` # 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 = { "brand": "Nike", "keywords": [ "Nike" ], "hashtags": [ "nike" ], "competitors": [], "maxPostsPerPlatform": 10, "maxCommentsPerPost": 50, "maxNewsPerQuery": 50, "maxItems": 0, "llmModel": "gpt-4o-mini" }; // Run the Actor and wait for it to finish const run = await client.actor("s4ndid/all-in-one-market-sentiment-marker").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 = { "brand": "Nike", "keywords": ["Nike"], "hashtags": ["nike"], "competitors": [], "maxPostsPerPlatform": 10, "maxCommentsPerPost": 50, "maxNewsPerQuery": 50, "maxItems": 0, "llmModel": "gpt-4o-mini", } # Run the Actor and wait for it to finish run = client.actor("s4ndid/all-in-one-market-sentiment-marker").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 '{ "brand": "Nike", "keywords": [ "Nike" ], "hashtags": [ "nike" ], "competitors": [], "maxPostsPerPlatform": 10, "maxCommentsPerPost": 50, "maxNewsPerQuery": 50, "maxItems": 0, "llmModel": "gpt-4o-mini" }' | apify call s4ndid/all-in-one-market-sentiment-marker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=s4ndid/all-in-one-market-sentiment-marker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ALL-IN-ONE market sentiment marker", "description": "Type in a brand, get one number back. Scans Instagram, TikTok, YouTube and the news, classifies every voice, and returns a single Market Sentiment Index (-100 to +100) plus competitor benchmarks. Free news tier, no API key.", "version": "0.0", "x-build-id": "OM5scncatT0xPHEGy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/s4ndid~all-in-one-market-sentiment-marker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-s4ndid-all-in-one-market-sentiment-marker", "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/s4ndid~all-in-one-market-sentiment-marker/runs": { "post": { "operationId": "runs-sync-s4ndid-all-in-one-market-sentiment-marker", "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/s4ndid~all-in-one-market-sentiment-marker/run-sync": { "post": { "operationId": "run-sync-s4ndid-all-in-one-market-sentiment-marker", "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": [ "brand" ], "properties": { "brand": { "title": "Brand / product / company", "type": "string", "description": "The brand, product, company or topic you want to measure sentiment for. This is the only required field." }, "keywords": { "title": "Search keywords", "uniqueItems": true, "type": "array", "description": "Extra search terms used for news and for TikTok/YouTube search. Defaults to the brand name. Add product lines, slogans or misspellings to widen the net.", "items": { "type": "string" } }, "hashtags": { "title": "Hashtags", "uniqueItems": true, "type": "array", "description": "Hashtags used to discover Instagram and TikTok posts (without the `#`). Defaults to the brand name with spaces stripped.", "items": { "type": "string" } }, "platforms": { "title": "Platforms to scan", "uniqueItems": true, "type": "array", "description": "Which channels to listen to. **News is free.** Instagram, TikTok and YouTube each call a paid public Actor, billed to your account โ€” see the README for costs.", "items": { "type": "string", "enum": [ "news", "instagram", "tiktok", "youtube" ], "enumTitles": [ "News โ€” Google News (free, no extra cost)", "Instagram โ€” posts + comments (paid Actor)", "TikTok โ€” posts + comments (paid Actor)", "YouTube โ€” videos + comments (paid Actor)" ] }, "default": [ "news" ] }, "competitors": { "title": "Competitors to benchmark", "uniqueItems": true, "type": "array", "description": "Optional. Each competitor is scored the same way (via news) so you get a side-by-side sentiment comparison against your brand.", "items": { "type": "string" } }, "maxPostsPerPlatform": { "title": "Max posts per platform", "minimum": 1, "maximum": 200, "type": "integer", "description": "How many posts/videos to discover per social platform. Drives cost โ€” each post's comments are then scraped.", "default": 10 }, "maxCommentsPerPost": { "title": "Max comments per post", "minimum": 1, "maximum": 500, "type": "integer", "description": "How many comments to collect from each discovered post/video.", "default": 50 }, "maxNewsPerQuery": { "title": "Max news headlines per keyword", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many news headlines to pull per keyword (and per competitor). Free.", "default": 50 }, "maxItems": { "title": "Maximum dataset items", "minimum": 0, "type": "integer", "description": "Hard cap on rows pushed to the dataset. `0` = no limit. The sentiment marker row is always written first.", "default": 0 }, "includeRawItems": { "title": "Include raw posts / comments / headlines", "type": "boolean", "description": "Also output every individual post, comment and headline with its own sentiment label. Turn off if you only want the scores.", "default": true }, "sentimentEngine": { "title": "Sentiment engine", "enum": [ "lexicon", "llm" ], "type": "string", "description": "**Lexicon** is fast, free and deterministic (no API key). **LLM** additionally re-checks the items the lexicon calls neutral, using your own OpenAI-compatible key.", "default": "lexicon" }, "openaiApiKey": { "title": "OpenAI API key", "type": "string", "description": "Only used when the engine is set to LLM. Your key is used to re-classify ambiguous items and is never stored." }, "llmModel": { "title": "LLM model", "type": "string", "description": "Model name for the LLM refine step.", "default": "gpt-4o-mini" }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Optional proxy for the direct (news) requests. Usually unnecessary.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```