# AI Media Monitor – Brand, News & Sentiment Tracker (`lofomachines/ai-media-monitor`) Actor Track brands, products, competitors and topics across global news coverage. Get clean, structured results enriched with AI sentiment, summaries and an executive intelligence report. - **URL**: https://apify.com/lofomachines/ai-media-monitor.md - **Developed by:** [Lofomachines](https://apify.com/lofomachines) (community) - **Categories:** AI, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 news 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 ## AI Media Monitor – Brand, News & Sentiment Tracker 📰🤖 **Track your brand, products, competitors and any topic across global news coverage — and get clean, structured results enriched with AI sentiment, summaries and an executive intelligence report. No accounts. No logins. No setup headaches.** AI Media Monitor turns the noise of the world's news into a tidy, decision-ready dataset. Give it the names you care about, pick a market and a time range, and receive every relevant article with sentiment, a one-line AI summary, topics, key entities and a relevance score — plus a ready-to-read intelligence report for the whole run. Whether you are protecting a reputation, watching competitors, spotting trends, or feeding a dashboard, this Actor delivers the structured media data you need in minutes. --- ### ✨ Why people use AI Media Monitor - **🌍 Global coverage** — monitor news across 20 markets and 9 languages. - **🧠 AI sentiment & summaries** — every article gets a sentiment label, a polarity score, a short summary, topics, a category, key entities and a relevance score. - **📊 Executive intelligence report** — a run-level brief with overall tone, key themes, notable developments, emerging narratives and recommended actions. - **🎯 Smart relevance filtering** — describe your brand once and the AI separates real coverage from unrelated namesakes. - **⚡ Fast & affordable** — optimized to return rich results at a very low cost per run. - **🔌 Zero authentication** — no API keys to chase, no platform logins, no cookies. - **🧱 Clean, structured output** — perfect for spreadsheets, BI tools, CRMs, Slack alerts, or your own app. --- ### 🚀 What you get For **every article**: | Field | Description | |---|---| | `keyword` | The monitored term that matched | | `title` | Clean headline | | `url` | Direct link to the article | | `publisher` | Name of the outlet | | `sourceDomain` | Outlet domain | | `publishedAt` | Publication date (ISO 8601) | | `snippet` | Short preview text | | `sentiment` | `positive` / `neutral` / `negative` | | `sentimentScore` | Polarity from -1 to 1 | | `summary` | One-to-two sentence AI summary | | `topics` | AI-extracted tags | | `category` | High-level category | | `entities` | Companies, people, products mentioned | | `relevanceScore` | 0–100 relevance to your term | | `isRelevant` | Whether it is genuinely about your term | | `country` / `language` | Market and language of the coverage | | `collectedAt` | When the result was collected | Plus a run-level **AI intelligence report** (`REPORT`) containing the executive summary, sentiment breakdown, top publishers, key themes, notable developments, emerging narratives and recommended actions. --- ### 🧰 Use cases AI Media Monitor was built to serve many teams from a single, simple input: - **Brand & reputation monitoring** — know what's being said about you, the moment it's published. - **Crisis & PR detection** — surface negative coverage early and react fast. - **Competitor intelligence** — track rivals' launches, partnerships and momentum. - **Market & industry research** — follow a whole topic, sector or trend over time. - **Investment & due diligence** — gauge the news narrative around a company or asset. - **Content & social media** — find fresh angles and trending stories to write about. - **Lead & opportunity signals** — spot expansions, funding and hiring news. - **Executive & board briefings** — auto-generate a clean media digest on schedule. - **Academic & policy research** — collect structured coverage for analysis. --- ### 📝 How to use it 1. **What to monitor** — add one or more terms (brands, products, people, competitors, topics). Use quotes for exact phrases, e.g. `"electric vehicles"`. 2. **Country / market** — choose the market you want coverage from. 3. **Language** — pick the coverage language. 4. **Time range** — past 24 hours, week, month, or any time. 5. **Max results per keyword** — control depth vs. cost. 6. **Enable AI analysis** — keep on for sentiment, summaries and the report. 7. **Context (optional)** — describe your brand so relevance scoring is sharper. 8. **Report language** — language for AI summaries and the report. Click **Start** and collect your results from the **Output** tab and the **REPORT** record. #### Example input ```json { "keywords": ["Tesla", "Rivian", "electric vehicles"], "country": "US", "language": "en", "timeframe": "d7", "maxArticlesPerKeyword": 50, "includeSentimentAnalysis": true, "brandContext": "Tesla, the electric vehicle maker. Focus on product, business and reputation news.", "outputLanguage": "English" } ```` *** ### 🔁 Automate it Run AI Media Monitor on a schedule (hourly, daily, weekly) to build a living media archive, power alerts, or keep a dashboard fresh. Connect it to Slack, email, Google Sheets, or your data warehouse using Apify integrations and webhooks. *** ### 💡 Tips for best results - Add a short **Context** sentence — it dramatically improves relevance scoring for common names. - Use **quotes** for multi-word brands to avoid noise. - Start with **Past week** and a moderate result limit, then widen once you see the quality. - Monitor **competitors alongside your brand** to benchmark share of voice and tone. *** ### ❓ FAQ **Do I need any account or login?** No. The Actor collects publicly available coverage without any authentication. **Which markets and languages are supported?** 20 markets (US, UK, Canada, Australia, Ireland, India, South Africa, Singapore, UAE, Germany, France, Spain, Italy, Netherlands, Portugal, Brazil, Mexico, Argentina, Japan, Saudi Arabia) and 9 languages. **Can I run it without AI analysis?** Yes. Turn off **Enable AI analysis** to receive raw coverage only. **How fresh is the data?** You choose the time range; the most recent coverage is prioritized. **Is the output structured?** Yes — a clean, consistent dataset (with multiple views) plus a structured intelligence report. *** ### 📦 Output formats Export your results as **JSON, CSV, Excel, HTML or XML**, or pull them via the **Apify API** for seamless integration into your stack. *** **Stop drowning in tabs. Start monitoring the news that matters — with sentiment, summaries and insight, automatically.** 🚀 # Actor input Schema ## `keywords` (type: `array`): Add the brands, products, people, competitors or topics you want to track. Each line is monitored separately. Use quotes for exact matches, e.g. "Tesla Model 3". ## `country` (type: `string`): The market you want coverage from. This shapes which outlets and regional stories are included. ## `language` (type: `string`): Preferred language of the coverage to collect. ## `timeframe` (type: `string`): How far back to look for coverage. ## `maxArticlesPerKeyword` (type: `integer`): Upper limit of articles collected for each monitored term. Lower it to save credits, raise it for deeper coverage. ## `includeSentimentAnalysis` (type: `boolean`): Adds sentiment, a short summary, topics, category, key entities and relevance score to every result, plus an executive intelligence report. Turn off for raw coverage only. ## `brandContext` (type: `string`): Describe your brand or topic in one or two sentences so the AI can score relevance and sentiment more accurately. Example: 'Nike, the sportswear brand. We care about product launches and reputation, not unrelated people named Nike.' ## `outputLanguage` (type: `string`): Language used for AI summaries and the executive report. ## Actor input object example ```json { "keywords": [ "Nike", "running shoes", "Adidas" ], "country": "US", "language": "en", "timeframe": "d7", "maxArticlesPerKeyword": 10, "includeSentimentAnalysis": false, "outputLanguage": "English" } ``` # Actor output Schema ## `coverage` (type: `string`): Every collected article enriched with sentiment, summary, topics, category, entities and relevance score. ## `report` (type: `string`): Executive summary, sentiment breakdown, key themes, notable developments and recommended actions for the whole run. # 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 = { "keywords": [ "artificial intelligence" ] }; // Run the Actor and wait for it to finish const run = await client.actor("lofomachines/ai-media-monitor").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 = { "keywords": ["artificial intelligence"] } # Run the Actor and wait for it to finish run = client.actor("lofomachines/ai-media-monitor").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 '{ "keywords": [ "artificial intelligence" ] }' | apify call lofomachines/ai-media-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lofomachines/ai-media-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Media Monitor – Brand, News & Sentiment Tracker", "description": "Track brands, products, competitors and topics across global news coverage. Get clean, structured results enriched with AI sentiment, summaries and an executive intelligence report.", "version": "0.1", "x-build-id": "TOgVS6dVsB9ZWmPUm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lofomachines~ai-media-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lofomachines-ai-media-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lofomachines~ai-media-monitor/runs": { "post": { "operationId": "runs-sync-lofomachines-ai-media-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lofomachines~ai-media-monitor/run-sync": { "post": { "operationId": "run-sync-lofomachines-ai-media-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "What to monitor", "type": "array", "description": "Add the brands, products, people, competitors or topics you want to track. Each line is monitored separately. Use quotes for exact matches, e.g. \"Tesla Model 3\".", "items": { "type": "string" } }, "country": { "title": "Country / market", "enum": [ "US", "GB", "CA", "AU", "IE", "IN", "ZA", "SG", "AE", "DE", "FR", "ES", "IT", "NL", "PT", "BR", "MX", "AR", "JP", "SA" ], "type": "string", "description": "The market you want coverage from. This shapes which outlets and regional stories are included.", "default": "US" }, "language": { "title": "Language", "enum": [ "en", "de", "fr", "es", "it", "nl", "pt", "ja", "ar" ], "type": "string", "description": "Preferred language of the coverage to collect.", "default": "en" }, "timeframe": { "title": "Time range", "enum": [ "d1", "d7", "d30", "any" ], "type": "string", "description": "How far back to look for coverage.", "default": "d7" }, "maxArticlesPerKeyword": { "title": "Max results per keyword", "minimum": 10, "maximum": 100, "type": "integer", "description": "Upper limit of articles collected for each monitored term. Lower it to save credits, raise it for deeper coverage.", "default": 10 }, "includeSentimentAnalysis": { "title": "Enable AI analysis", "type": "boolean", "description": "Adds sentiment, a short summary, topics, category, key entities and relevance score to every result, plus an executive intelligence report. Turn off for raw coverage only.", "default": false }, "brandContext": { "title": "Context (optional)", "type": "string", "description": "Describe your brand or topic in one or two sentences so the AI can score relevance and sentiment more accurately. Example: 'Nike, the sportswear brand. We care about product launches and reputation, not unrelated people named Nike.'" }, "outputLanguage": { "title": "Report language", "type": "string", "description": "Language used for AI summaries and the executive report.", "default": "English" } } }, "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 } } } } } } } } } } ```