As part of the NewsCatcher processing pipeline, each article is enriched with NLP data before it is indexed: theme classification, sentiment scores, named entities, content tags, and vector embeddings. News API exposes these fields in the response when you setDocumentation Index
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include_nlp_data to true.
How NLP processing works
Processing mode depends on the article’s language and determines which response fields are populated and which arenull.
Native processing applies to English and Arabic articles. NLP runs on the
original text and results appear in the standard nlp.* fields.
Translation-based processing applies to all other languages. The article is
first translated to English, then NLP runs on that translation. Results appear
in nlp.translation_* fields — the corresponding standard fields are explicitly
null, not absent. To receive translation fields in the response, set
include_translation_fields to true.
This distinction matters when consuming NER or summary fields: a null value in
nlp.ner_PER means the article was processed via translation, not that no
entities exist — check nlp.translation_ner_PER instead.
Available features
| Feature | What it produces |
|---|---|
| Theme | One or more topic labels per article, for example Tech or Finance. Filterable with theme and not_theme. |
| Summary | AI-generated article summary. nlp.summary for native, nlp.translation_summary for translation-based. |
| Sentiment | Tone scores from -1.0 to 1.0 for title and content independently. |
| Named entity recognition | Persons, organizations, locations, and miscellaneous entities with mention counts. |
| IPTC tags | Hierarchical news category tags using the IPTC media topic standard. |
| IAB tags | Content category tags using the IAB content taxonomy, used for audience segmentation. |
| Custom tags | Organization-specific taxonomy, private to your API key. |
| Vector embeddings | 1024-dimensional semantic vectors for similarity search and clustering. |