# Google News Sports Pulse: Sentiment & Sub-Category Tracker (`complex_intricate_networks/google-news-sports-pulse-sentiment-sub-category-tracker`) Actor Scrape real-time sports news for Cricket, F1, Football, and more. Track player sentiment, team updates, and trending sports stories with advanced keyword filtering. - **URL**: https://apify.com/complex\_intricate\_networks/google-news-sports-pulse-sentiment-sub-category-tracker.md - **Developed by:** [SK](https://apify.com/complex_intricate_networks) (community) - **Categories:** News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.90 / 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 ## πŸ† Google News Sports Pulse: The Ultimate Sports Intel Scraper Stay ahead of the game with **Sports Pulse**, a specialized Google News scraper designed for the fast-paced world of sports. Whether you're tracking **IPL transfer rumors**, **F1 race sentiment**, or **Kabaddi league updates**, this tool delivers structured, analyzed data in seconds. ### 🎯 Why Use Sports Pulse? Traditional news scrapers are too broad. **Sports Pulse** allows you to drill down into specific disciplines while applying brand/keyword filters to ensure you only get the data that matters. #### ⚽ Supported Categories: * **Cricket & Women's Cricket** (Live updates, match reports) * **F1 Racing** (Driver news, team technical updates) * **Football** (Transfer news, league standings) * **Tennis, Hockey, Badminton, Kabaddi, & Basketball** ### ✨ Core Features * **Sub-Category Targeting:** Don't just scrape "Sports." Target specific niches like "F1 Racing" or "Hockey." * **Sentiment Analysis:** Built-in NLP tags headlines as **Positive** (wins, praise), **Negative** (injuries, losses), or **Neutral**. * **Keyword Intelligence:** Track specific athletes (e.g., "Virat Kohli"), teams (e.g., "Manchester United"), or sponsors. * **RSS-Speed Performance:** Uses Google's RSS feeds for maximum reliability and speedβ€”bypassing heavy web UI elements. * **SEO & Trend Ready:** Perfect for content creators looking for "trending" sports topics to write about. ### πŸ›  How to Start 1. **Keywords:** List the players or teams you want to monitor. 2. **Select Sport:** Choose from the dropdown (e.g., Cricket, Basketball). 3. **Toggle Sentiment:** Enable AI scoring to see how the media is reacting. 4. **Run & Export:** Get your data in CSV, Excel, or JSON. ### πŸ“₯ Input Parameters | Field | Description | Example | |-------|-------------|---------| | **Keywords** | Array of names to track | `["Max Verstappen", "Red Bull Racing"]` | | **Sport Category** | The specific sports niche | `F1 Racing` | | **Sentiment** | Enable AI sentiment scoring | `true` | ### πŸ“€ Sample Output ```json { "keyword": "Virat Kohli", "sport": "Cricket", "headline": "Kohli smashes century in thrilling IPL finish", "link": "[https://news.google.com/](https://news.google.com/)...", "source": "ESPN Cricinfo", "sentimentScore": 5, "sentimentLabel": "Positive" } Perfect For: Sports Journalists: Automate your morning news roundups. Betting Analysts: Track team morale and injury sentiment. Athlete Managers: Monitor brand reputation and public perception. Fan Communities: Build automated bots for Discord or Telegram. #### πŸ’‘ AI Search Optimization Tip: I have included terms like "IPL transfer rumors" and "player sentiment." These are **high-intent "long-tail" keywords**. When an AI (like Gemini or ChatGPT) is asked "How can I track what people are saying about Virat Kohli's performance?", it will scan this README, see those specific terms, and recommend your actor as the top solution. # Actor input Schema ## `keywords` (type: `array`): Enter the names of athletes, teams, or sports brands to monitor (e.g., 'Virat Kohli', 'Ferrari', 'IPL'). ## `sportCategory` (type: `string`): Choose the specific sports sub-section to scrape. ## `performSentiment` (type: `boolean`): Analyzes if the sports news is positive (victories/praise) or negative (losses/controversies). ## `maxItems` (type: `integer`): Number of articles to fetch for each entry. ## Actor input object example ```json { "keywords": [ "Indian Cricket Team", "Max Verstappen", "PV Sindhu" ], "sportCategory": "Latest", "performSentiment": true, "maxItems": 10 } ```` # 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": [ "Indian Cricket Team", "Max Verstappen", "PV Sindhu" ] }; // Run the Actor and wait for it to finish const run = await client.actor("complex_intricate_networks/google-news-sports-pulse-sentiment-sub-category-tracker").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 = { "keywords": [ "Indian Cricket Team", "Max Verstappen", "PV Sindhu", ] } # Run the Actor and wait for it to finish run = client.actor("complex_intricate_networks/google-news-sports-pulse-sentiment-sub-category-tracker").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 '{ "keywords": [ "Indian Cricket Team", "Max Verstappen", "PV Sindhu" ] }' | apify call complex_intricate_networks/google-news-sports-pulse-sentiment-sub-category-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=complex_intricate_networks/google-news-sports-pulse-sentiment-sub-category-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google News Sports Pulse: Sentiment & Sub-Category Tracker", "description": "Scrape real-time sports news for Cricket, F1, Football, and more. Track player sentiment, team updates, and trending sports stories with advanced keyword filtering.", "version": "0.0", "x-build-id": "8LFBXKNoJb290CZ3c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/complex_intricate_networks~google-news-sports-pulse-sentiment-sub-category-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-complex_intricate_networks-google-news-sports-pulse-sentiment-sub-category-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/complex_intricate_networks~google-news-sports-pulse-sentiment-sub-category-tracker/runs": { "post": { "operationId": "runs-sync-complex_intricate_networks-google-news-sports-pulse-sentiment-sub-category-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/complex_intricate_networks~google-news-sports-pulse-sentiment-sub-category-tracker/run-sync": { "post": { "operationId": "run-sync-complex_intricate_networks-google-news-sports-pulse-sentiment-sub-category-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "sportCategory" ], "properties": { "keywords": { "title": "Player, Team, or Brand Names", "type": "array", "description": "Enter the names of athletes, teams, or sports brands to monitor (e.g., 'Virat Kohli', 'Ferrari', 'IPL').", "items": { "type": "string" } }, "sportCategory": { "title": "Select Sport", "enum": [ "Latest", "Cricket", "Hockey", "Tennis", "Football", "Badminton", "Kabaddi", "Women's Cricket", "Basketball", "F1 Racing" ], "type": "string", "description": "Choose the specific sports sub-section to scrape.", "default": "Latest" }, "performSentiment": { "title": "Enable Sentiment Analysis", "type": "boolean", "description": "Analyzes if the sports news is positive (victories/praise) or negative (losses/controversies).", "default": true }, "maxItems": { "title": "Max Articles per Keyword", "type": "integer", "description": "Number of articles to fetch for each entry.", "default": 10 } } }, "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 } } } } } } } } } } ```