News Mention Alert Engine

📰 News Mention Alert Engine is a powerful Apify Actor designed to monitor, detect, and analyze News Mention data from Google News across brands, products, persons, and keywords. This tool provides real-time News Mention alerts with sentiment analysis, entity extraction, and comprehensive intelligence metrics. Whether you're monitoring brand reputation, tracking competitors, or conducting market intelligence, the News Mention Alert Engine delivers actionable News Mention insights efficiently.

With Google News RSS integration, multi-dimensional search (brands/products/persons/keywords), advanced NLP sentiment analysis, entity extraction, deduplication, and PPE billing support, the News Mention Alert Engine ensures comprehensive News Mention discovery and tracking. It focuses on key News Mention metrics including sentiment, relevance, entity mentions, and alert classification, making it an essential tool for News Mention research and reputation management.

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📋 Table of Contents

• Features
• How It Works
• Input
• Output
• Technical Stack
• Data Fields
• Sentiment Analysis
• Entity Extraction
• Use Cases
• Quick Start
• Configuration
• Performance
• Billing
• Important Notes
• Keywords
• Changelog
• Support

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🔥 Features

• Multi-Dimensional Search – Search News Mention across brands, products, persons, and keywords simultaneously.
• Google News Integration – Direct extraction of News Mention from Google News RSS feeds.
• Brand Monitoring – Track brand mentions in real-time news.
• Product Monitoring – Monitor product launches, reviews, and discussions.
• Person Monitoring – Track mentions of executives, influencers, or public figures.
• Keyword Monitoring – Monitor general keywords and industry terms.
• Sentiment Analysis – Advanced NLP sentiment classification (positive/negative/neutral).
• Sentiment Scoring – Quantitative sentiment score for each News Mention.
• Relevance Scoring – Ranks mentions by relevance to queries.
• Entity Extraction – Automatically extracts companies, people, and locations.
• Alert Classification – Categorizes mentions by type (brand, product, person, keyword).
• Deduplication – Removes duplicate News Mention across searches.
• Duplicate Tracking – Identifies new vs. previously seen mentions via KeyValueStore.
• News Source Tracking – Captures and ranks news sources.
• Publication Date – Records when News Mention was published.
• News URL Capture – Captures direct links to News Mention articles.
• Summary Generation – Generates comprehensive News Mention summaries.
• Sentiment Distribution – Calculates sentiment percentages.
• Top Entities – Identifies top mentioned companies and people.
• Positive/Negative Highlights – Extracts top positive and negative News Mention.
• PPE Billing Integration – Pay-per-event billing per News Mention detected.
• Charge Limit Handling – Respects user's maximum PPE charge limits.
• Proxy Support – Apify residential proxy support for reliability.
• Real-Time Dataset Push – Pushes results to Apify Dataset with metadata.
• Detailed Logging – Comprehensive logging of charges and progress.
• Asyncio-Friendly – Non-blocking async/await architecture.

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⚙️ How It Works

The News Mention Alert Engine accepts lists of brands, products, persons, and keywords to monitor. It queries Google News RSS for each search term, fetches articles, performs sentiment analysis, extracts entities, and classifies alert types. Mentions are deduplicated using persistent KeyValueStore to track previously seen articles. New mentions are identified and highlighted. All mentions are analyzed and charged via PPE billing. A comprehensive summary is generated with sentiment distribution, top entities, and highlights.

Key Processing Steps:

1. Input Parsing – Accept brands, products, persons, keywords
2. Proxy Setup – Configure Apify residential proxy
3. Query Generation – Build search queries from inputs
4. KeyValueStore Load – Load previously seen mention IDs
5. News Fetching – Query Google News RSS for each search term
6. Article Parsing – Extract title, description, source, date, URL
7. Sentiment Analysis – Analyze sentiment for each News Mention
8. Entity Extraction – Extract companies, people, locations
9. Alert Classification – Categorize mention types
10. Deduplication – Check against seen mentions
11. New Detection – Identify new vs. previously seen
12. Relevance Scoring – Score mention relevance
13. Summary Generation – Build comprehensive summary
14. Dataset Push – Push summary and mentions
15. PPE Charging – Charge per mention
16. KeyValueStore Update – Update seen mention IDs

Key Benefits:

• Monitor News Mention across multiple dimensions simultaneously
• Detect reputation threats early
• Track competitor activity in real-time
• Understand public perception via sentiment
• Identify key influencers and industry leaders
• Make informed business decisions
• Automate brand monitoring

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📥 Input

The Actor accepts the following input parameters:

Field	Type	Default	Description
brands	array/string	[]	Brands to monitor (comma-separated or array)
products	array/string	[]	Products to monitor (comma-separated or array)
persons	array/string	[]	Persons to monitor (comma-separated or array)
keywords	array/string	[]	Keywords to monitor (comma-separated or array)
maxPerQuery	integer	30	Maximum News Mention per search query (1-100)
useApifyProxy	boolean	true	Enable Apify residential proxies
apifyProxyGroups	array	["RESIDENTIAL"]	Proxy group configuration

Example Input:

{
  "brands": ["Apple", "Samsung", "Google"],
  "products": ["iPhone", "Galaxy", "Pixel"],
  "persons": ["Tim Cook", "Satya Nadella"],
  "keywords": ["AI", "machine learning"],
  "maxPerQuery": 30,
  "useApifyProxy": true
}

Brand Monitoring Example:

{
  "brands": "Tesla, Ford, GM",
  "maxPerQuery": 50
}

Executive Tracking Example:

{
  "persons": ["Elon Musk", "Bill Gates", "Jeff Bezos"],
  "maxPerQuery": 25
}

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📤 Output

The Actor pushes News Mention records with the following structure:

Individual Mention Record:

Field	Type	Description
query	string	Search query that found this mention
mention_type	string	Type of mention (brand/product/person/keyword)
alert_types	array	Classification tags (e.g., ["brand:Apple", "keyword:AI"])
title	string	News Mention headline
description	string	Article summary (400 chars max)
source	string	News source publication
url	string	Direct link to article
published	string	Publication date/time
sentiment	string	Sentiment (positive/negative/neutral)
sentiment_score	integer	Quantitative sentiment score
relevance	integer	Relevance score (1-3)
entities	object	Extracted companies, people, locations
is_new	boolean	Whether this is a new mention
id	string	Unique mention identifier
scraped_at	string	ISO 8601 scrape timestamp

Summary Record:

Comprehensive analysis including:

• Total and new mention counts
• Sentiment distribution and percentages
• Top news sources
• Alert type breakdown
• Top mentioned companies and people
• Top positive and negative news highlights

Example Individual Mention:

{
  "query": "Apple",
  "mention_type": "brand",
  "alert_types": ["brand:Apple", "product:iPhone"],
  "title": "Apple Announces Revolutionary New iPhone Features",
  "description": "Apple introduced groundbreaking features in the latest iPhone release, marking a significant innovation...",
  "source": "Tech News Daily",
  "url": "https://technewsdaily.com/article/...",
  "published": "2025-02-14T08:30:00",
  "sentiment": "positive",
  "sentiment_score": 4,
  "relevance": 3,
  "entities": {
    "companies": ["Apple Inc.", "Samsung Electronics"],
    "people": ["Tim Cook", "Craig Federighi"],
    "locations": ["San Francisco", "Cupertino"]
  },
  "is_new": true,
  "id": "guid-12345...",
  "scraped_at": "2025-02-14T12:00:00"
}

Example Summary:

{
  "type": "SUMMARY",
  "config": {
    "brands": ["Apple", "Samsung"],
    "products": ["iPhone", "Galaxy"],
    "persons": ["Tim Cook"],
    "keywords": ["AI"]
  },
  "summary": {
    "generated_at": "2025-02-14T12:00:00",
    "total_mentions": 127,
    "new_mentions": 34,
    "sentiment": {
      "positive": 78,
      "negative": 31,
      "neutral": 18,
      "positive_pct": 61.4,
      "negative_pct": 24.4
    },
    "top_sources": [
      {"source": "Tech News Daily", "count": 28},
      {"source": "Business Journal", "count": 19}
    ],
    "alert_type_breakdown": {
      "brand:Apple": 45,
      "product:iPhone": 32,
      "keyword:AI": 28,
      "person:Tim Cook": 15
    },
    "top_companies_mentioned": ["Apple", "Samsung", "Google"],
    "top_people_mentioned": ["Tim Cook", "Craig Federighi"],
    "top_positive_news": [
      {
        "title": "Apple Achieves Record Profits",
        "source": "Financial Times",
        "url": "https://..."
      }
    ],
    "top_negative_news": [
      {
        "title": "Apple Faces New Privacy Lawsuit",
        "source": "Legal News",
        "url": "https://..."
      }
    ]
  }
}

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🧰 Technical Stack

• News Source: Google News RSS feeds
• NLP/Sentiment: Custom word-based sentiment analysis
• Entity Extraction: Regex pattern matching
• Persistence: Apify KeyValueStore for deduplication
• Async: asyncio for non-blocking operations
• Data Structure: Collections (Counter) for aggregation
• Proxy: Apify Proxy with RESIDENTIAL configuration
• Logging: Apify Actor logging system
• Platform: Apify Actor serverless environment
• Billing: Apify PPE (Pay-Per-Event) system

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📊 Sentiment Analysis

Methodology

The Actor uses word-based sentiment analysis:

Positive Words: growth, profit, success, award, launch, partnership, expansion, innovation, excellent, leading, etc.

Negative Words: loss, decline, crash, scandal, lawsuit, fine, bankruptcy, layoff, investigation, fraud, failure, crisis, etc.

Scoring:

• Count matches in positive and negative word sets
• If positive > negative: sentiment = "positive", score = difference
• If negative > positive: sentiment = "negative", score = difference
• Otherwise: sentiment = "neutral", score = 0

Examples

Positive mention:

Title: "Apple Announces Record Profits and Expansion Plans"
Positive words: 2 (record, expansion, profits)
Negative words: 0
Sentiment: Positive, Score: 2

Negative mention:

Title: "Apple Faces Lawsuit Over Data Breach"
Positive words: 0
Negative words: 2 (lawsuit, breach)
Sentiment: Negative, Score: 2

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🔍 Entity Extraction

Extracted Entities

Companies: Matches patterns like "Name Corp", "Name Inc", "Name Ltd"

People: Matches patterns like "John Smith" or "CEO Jane Doe"

Locations: Matches known major cities (New York, London, Silicon Valley, Beijing, etc.)

Deduplication

Uses KeyValueStore to maintain set of seen mention IDs, enabling:

• Detection of new mentions on subsequent runs
• Avoidance of duplicate processing
• Persistent tracking across Actor invocations

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🎯 Use Cases

• Brand Reputation Monitoring – Monitor News Mention of your brand in real-time
• Competitor Intelligence – Track competitor News Mention and activities
• Executive Tracking – Monitor mentions of company executives and industry leaders
• Product Launch Tracking – Monitor News Mention around product launches
• Crisis Detection – Identify reputation threats early via sentiment analysis
• Market Research – Research industry trends via News Mention analysis
• PR Effectiveness – Measure PR campaign impact via News Mention volume
• Sentiment Analysis – Understand public perception via sentiment metrics
• Entity Discovery – Identify key companies and people mentioned with your brand
• News Aggregation – Aggregate relevant News Mention for team review
• Alert System – Create automated alerts for important News Mention
• Competitive Positioning – Understand competitive News Mention landscape
• Investor Relations – Track investor-relevant News Mention
• Marketing Intelligence – Inform marketing strategy with News Mention data
• Business Development – Identify partnership opportunities via News Mention

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🚀 Quick Start

1. Prepare Input

Go to Apify Console and enter:

{
  "brands": ["Apple", "Samsung"],
  "products": ["iPhone", "Galaxy"],
  "persons": "Tim Cook",
  "keywords": ["AI", "machine learning"],
  "maxPerQuery": 30,
  "useApifyProxy": true
}

2. Run the Actor

Click Start button. The Actor will:

• Search Google News for all queries
• Analyze sentiment for each News Mention
• Extract entities
• Deduplicate against historical mentions
• Generate summary
• Push to Dataset

3. Monitor Progress

Console shows:

Brands=["Apple", "Samsung"] | Products=["iPhone"] | Persons=["Tim Cook"] | Keywords=["AI"]
Proxy active: RESIDENTIAL
[BRAND] 'Apple'
  -> 28 articles found
[BRAND] 'Samsung'
  -> 24 articles found
[PRODUCT] 'iPhone'
  -> 18 articles found
[PERSON] 'Tim Cook'
  -> 12 articles found
[KEYWORD] 'AI'
  -> 45 articles found
✅ [CHARGED] Mention saved | Query: Apple | Sentiment: positive | Total: 1
✅ [CHARGED] Mention saved | Query: Samsung | Sentiment: neutral | Total: 2
...
🎉 Done! Total=127 | New=34 | Total charged=127 | Positive=78 | Negative=31

4. View & Download Results

• Results Tab: All News Mention records + summary
• Export: JSON, CSV, Excel
• Filter: By sentiment or mention type
• Sort: By date or relevance

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⚙️ Configuration

Search Scope

Brand focus:

{
  "brands": ["Apple", "Microsoft", "Google"]
}

Multi-dimensional:

{
  "brands": ["Tesla"],
  "products": ["Model 3", "Cybertruck"],
  "persons": ["Elon Musk"],
  "keywords": ["electric vehicles"]
}

Result Limits

Quick scan (10 per query):

{
  "maxPerQuery": 10
}

Comprehensive (50+ per query):

{
  "maxPerQuery": 50
}

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📈 Performance

Processing Speed

• ~2-3 seconds per query
• ~30-60 seconds total for 10-20 queries
• Summary generation adds ~5-10 seconds

Resource Usage

• Memory: ~80-150MB
• CPU: ~30-40% during processing
• Network: ~2-4MB per run
• API calls: 1 per search query

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💰 Billing

PPE (Pay-Per-Event) Billing

• Event Name: "scraped-result"
• Charge: 1 credit per News Mention detected
• Billing Trigger: Per mention article processed
• Typical Cost: 30-150 credits per run

Cost Examples

• 5 brands × 30 articles = 150 credits
• 3 brands + 2 products + 2 people + 3 keywords, 20 articles each = 200 credits

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⚠️ Important Notes

Legal & Compliance

• Fair Use: Respects Google News ToS and rate limits
• Attribution: Respects news source and author attribution
• Sentiment: Algorithmic analysis, not definitive
• Proxy: Recommended for reliability
• Rate Limiting: Includes appropriate delays

Data Quality

• Freshness: Real-time news data
• Completeness: Varies by search term popularity
• Accuracy: Sentiment is statistical analysis
• Entity: Pattern-based extraction, may have false positives
• Verification: Always verify with original sources

Best Practices

• Use residential proxies
• Monitor sentiment carefully (may be inaccurate)
• Verify important mentions independently
• Set reasonable query limits
• Check results regularly for news monitoring
• Combine with manual review
• Respect news source copyrights
• Follow news outlet guidelines

📦 Changelog

Initial Release:

• Google News RSS integration
• Multi-dimensional search (brands/products/persons/keywords)
• News article fetching and parsing
• Title and description extraction
• Source and publication date capture
• URL extraction
• Sentiment analysis (positive/negative/neutral)
• Sentiment scoring algorithm
• Entity extraction (companies, people, locations)
• Alert classification by mention type
• Relevance scoring (3 for exact match, 1 for query match)
• Deduplication using KeyValueStore
• New mention detection (vs previously seen)
• Summary generation
• Sentiment distribution calculation
• Top entity extraction
• Top positive/negative news identification
• PPE billing per mention
• Charge limit detection and stopping
• Configurable result limits per query
• Apify proxy support
• Real-time Dataset push
• Detailed progress logging
• Comprehensive error handling
• Asyncio executor support

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🧑‍💻 Support & Feedback

• Issues: Submit via Apify console with search terms
• Documentation: Check Actor details page
• Community: Apify forum discussions
• Feature Requests: Suggest improvements or news sources
• Bug Reports: Include brands/keywords and error details

📄 License & Legal

Terms of Use:

• Use for legitimate business and research
• Respect Google News ToS and rate limits
• Respect news source copyrights and attribution
• Don't republish articles without permission
• Comply with applicable laws
• Use data ethically and responsibly

Disclaimer: News Mention Alert Engine is provided as-is for monitoring purposes. Users are responsible for ensuring compliance with Google News ToS and applicable laws. Always verify information with original news sources.

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🎉 Get Started Today

Deploy now for News Mention monitoring!

Use for:

• 📊 Brand Monitoring
• 🔍 Reputation Management
• 💡 Competitor Intelligence
• 📈 Sentiment Analysis
• 🎯 Crisis Detection

Perfect for:

• Brand Managers
• PR Professionals
• Marketing Teams
• Business Development
• Analysts

Last Updated: February 2025
Version: 1.0.0
Status: Production Ready
Platform: Apify Actor
News Source: Google News RSS
Deduplication: KeyValueStore-based

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📚 Related Tools

• App Review Intelligence Monitor
• Export Intelligence Monitor
• Business Social Media Finder
• Developer Tools Scraper

📰 News Mention Excellence

This Actor is optimized for News Mention monitoring with:

• ✅ Multi-dimensional search
• ✅ Google News integration
• ✅ Advanced sentiment analysis
• ✅ Entity extraction
• ✅ Deduplication tracking
• ✅ Summary generation
• ✅ PPE billing support
• ✅ Real-time Dataset push
• ✅ Error recovery
• ✅ Production-ready code

Monitor news mentions effortlessly! 💎🚀