Hard News

The Hard News classifier is designed to distinguish between factual, authoritative news from opinion-based content. It identifies hard news by focusing on fact-based and timely events, while soft news typically covers opinion or less critical topics.

Statistics

Example Use Cases

The Hard News classifier identifies objective news articles versus subjective, more fact versus opinion-based, and consists of timely events. For example, using the hard news classifier on a keyword search on news sources can help the searcher find content about recent incidents while filtering out editorials, commentary, or opinion pieces that may contain those keywords but are not specific news reports.

Streaming Usage

Compatible Data Scoures

As a stream-integrated classifier, it is run on ingestion for specific sources.

Output

The Intent classifier is a binary label. The label value is ‘True’ if the news article is hard news, ‘False’ otherwise.

"enrichment": 
                { 
    "hard_news": {
      "label": "No",
      "confidence": 0.87
                    },
   

Post-Processing Usage

Compatible Data Sources

As a Post-Processing operation, it can be run on any data source.

📘

Recipe Available

View the below recipe to see it in action, and easily view how to integrate it into your own data pipeline.

Usage

This Operation allows a user to specify the destination field, source fields, and any separator.

{
    "query": {
		...
},
    "operations": [
        {
            "name": "hard_news",
            "destination_path": "operations.hard_news",
            "parameters": {
                "language": "enrichment.language",
                "main": "content.body"
            }
        }
    ]
}
```