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Apply Tisane Entity Extraction to Bright Data X(Twitter)
Top companies trust Datastreamer to integrate, enrich, join, and apply their web data needs.
About Tisane Entity Extraction
Detect mentions of people, organizations, locations, filenames, phone numbers, crypto addresses, and more.
Entities are elements of relevance or interest in the text. Tisane extracts both standard entities and those relevant to trust & safety/law enforcement applications.
Standard entities are names of people, their social roles, organizations, places, and so on. We also extract cryptocurrency addresses, bank accounts, credit card numbers, phone numbers, software package names, and more.
Every entity entry is an object made of:
type - the type of the entity
name - a standard name, if exists; otherwise, the string that was logged
subtypes - more detailed additional types
subtype - the first subtype (for backward compatibility purposes)
mentions - an array of all detected mentions, with:
offset
length
sentence_index
text
wikidata - a Wikidata ID, if exists
About Bright Data X(Twitter)
Extract data about posts, profiles, likes, and more from X(Twitter).
Quickly apply Tisane Entity Extraction to Bright Data X(Twitter) with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Bright Data X(Twitter)
Web data is a critical input for enterprise data integration pipelines. Organizations can ingest data from multiple sources—including our partner network, internal enterprise systems, and publicly available web data—to create a unified, scalable data infrastructure.
Step 2
Add Tisane Entity Extraction with an Operation
Accelerate your web data workflows with Datastreamer. Whether it's enriching, joining, filtering, or storing your data, choose from hundreds of pre-built operations ready to deploy instantly.
Step 3
That's it! You have just connected Tisane Entity Extraction and Bright Data X(Twitter)
Using web data has never been this easy—thanks to Datastreamer. Expand your Pipelines on the fly and overcome the workflow challenges that once slowed you down.