Every article arrives pre-tagged.
No enrichment pipeline required.
News data enrichment is metadata attached to articles at the point of indexing, so the content arriving in your platform carries structured context alongside the text. Without it, every article is raw material: you know what was written, but not by whom, about which entities, on which topic, or how many people read it. Enrichment answers those questions before the article reaches your system.
The four layers below work independently or in combination.
Estimated reader reach per article, calculated from site traffic data and average article lifespan. Included at the article and site level.
IPTC topic codes and named entity tags (persons, organisations, locations) attached to every article, with LEI, FIGI, PermID, and Wikidata IDs where available.
Positive, negative, or neutral score per article. Available for English, Swedish, Danish, and Norwegian.
Dedicated daily monitoring of your priority sources, manual CAPTCHA bypass, paywall access via publisher agreements, and a named contact in the source team.
Related
Frequently Asked Questions
What is news data enrichment?
News data enrichment is the process of attaching structured metadata to articles at the point of indexing, before the content reaches your platform. Enrichment fields include topic classifications, entity tags with corporate identifiers, readership scores, and sentiment scores. Pre-attached enrichment removes the need to build or maintain a separate metadata pipeline on your end.
What is the difference between Topics and Entities and Readership?
Topics and Entities classify what an article is about and who it mentions, using IPTC topic codes and named entity tags linked to corporate identifiers such as LEI, FIGI, PermID, and Wikidata IDs. Readership estimates how many people saw a given article, based on site traffic data and average article lifespan.
The two serve different use cases: Topics and Entities power filtering, routing, and entity matching; Readership powers impact scoring and media analysis.
Do I need to build an enrichment pipeline myself?
No. Opoint attaches all enrichment metadata before delivering articles. Topics, entity tags, corporate identifiers, and readership scores arrive pre-attached in the feed, in a consistent JSON schema, across all sources, including non-English and regional outlets. The only configuration required is selecting which enrichment layers you want included.
Which enrichment layers work together?
All four layers are independent and can be combined in any configuration. Most integrations use Topics and Entities as a baseline. Readership is added for media monitoring and impact reporting workflows. Sentiment is available for English, Swedish, Danish, and Norwegian where tone classification is part of the use case. Service Packages are operational support and sit independently of the data enrichment layers.
Not sure which enrichment layer fits your use case?
Tell us what you are building or monitoring.
We will set up a call with a data specialist to point you to the right combination.
