Topic Trend Aggregator

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Topic Trend Aggregator

Try for free

Developed by

bySeitz AI & Automation

Maintained by Community

This actor is the central intelligence hub for a multi-pipeline news aggregation system. Its primary role is to fetch, unify, cleanse, and analyze raw news data from multiple Apify news pipeline actors, preparing a structured dataset of topical trends for downstream AI services.

0.0 (0)

Pricing

Pay per event

Last modified

18 hours ago

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Topic Trend Aggregator Actor (`topic_trend_aggregator_actor`)

This actor is the central intelligence hub for a multi-pipeline news aggregation system. Its primary role is to fetch, unify, cleanse, and analyze raw news data from multiple Apify news pipeline actors, preparing a structured dataset of topical trends for downstream AI services.

🎯 Key Features

Dynamic Pipeline Aggregation: Fetches the most recent successful datasets from an arbitrary list of user-selected news pipeline actors (e.g., Global Markets, AI, World News).
Data Cleansing: Performs rigorous deduplication based on article URL and title to ensure a clean, unique dataset.
LLM-Powered Analysis (Mocked): Performs the heavy lifting for topic clustering and scoring (currently mocked for cost efficiency but designed for integration with models like OpenAI):
- Extracts Key Entities (people, organizations, concepts).
- Clusters Similar Stories into distinct, named topics (e.g., "AI Regulation").
- Assigns a Trend Score (0-100) based on article volume, source diversity, and recency.
Structured Output: Delivers a clean, consistent JSON dataset for consumption by subsequent machine-learning and content generation actors.

⚙️ How It Fits into the Ecosystem

The topic_trend_aggregator_actor is the analytical layer that transforms raw data into actionable intelligence. Its output directly feeds the next stages of your content generation workflow:

Downstream Actor	Purpose
`keyword_opportunity_actor`	Consumes the topics and entities to identify high-potential, long-tail SEO keywords.
`sentiment_intel_actor`	Analyzes the articles within each trend to determine the prevailing sentiment (positive, negative, neutral).
`content_idea_generator_actor`	Uses the final trends, scores, keywords, and sentiment to generate high-quality, data-backed content ideas.

🛠️ Input Configuration

Field	Type	Description
`pipeline_actors`	`array` (`select`)	Required. Select the news pipeline actors whose data should be aggregated (e.g., Global Markets Intelligence).
`start_date`	`string`	Optional. Only include articles published after this date (ISO 8601 format).
`end_date`	`string`	Optional. Only include articles published before this date (ISO 8601 format).
`runTestMode`	`boolean`	If checked, the LLM analysis step is bypassed, and mock data is used for clustering/scoring (for development/cost saving).

🧱 Output Example

The actor outputs a list of aggregated trends, structured as follows:

[
  {
    "topic": "AI Regulation",
    "trend_score": 87,
    "category": "artificial_intelligence",
    "entities": ["EU", "OpenAI", "Regulation", "AI"],
    "articles": [
      {
        "title": "EU AI Act summary for business leaders",
        "url": "https://example.com/ai-act",
        "source": "TechCrunch",
        "published": "2025-10-11T08:00:00Z"
      }
      // ... more articles
    ]
  }
]

On this page

- Topic Trend Aggregator Actor (topic_trend_aggregator_actor)

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