Given a query, retrieve the most detected analytics, sorted by volume and grouped as follows:

  • companies: list[bigdata_client.models.entities.Company]
  • concepts: list[bigdata_client.models.entities.Concept]
  • languages: list[bigdata_client.models.languages.Language]
  • organizations: list[Union[bigdata_client.models.entities.Organization, bigdata_client.models.entities.OrganizationType]]
  • places: list[Union[bigdata_client.models.entities.Place, bigdata_client.models.entities.Facility, bigdata_client.models.entities.Landmark]]
  • products: list[Union[bigdata_client.models.entities.Product, bigdata_client.models.entities.ProductType]]
  • sources: list[bigdata_client.models.sources.Source]
  • topics: list[bigdata_client.models.topics.Topic]

This kind of method helps explore Bigdata content to find the needle in a haystack or research how the topic you are looking for impacts on other analytics:

from bigdata_client import Bigdata
from bigdata_client.query import Entity, Keyword

MICROSOFT = "228D42"

bigdata = Bigdata()
search = bigdata.search.new(query=(Entity(MICROSOFT) | Keyword("tesla")))
comentions = search.get_comentions()

It is also possible to work with comentions as a dictionary by using the method to_dict():

from bigdata_client import Bigdata
from bigdata_client.query import Entity, Keyword

MICROSOFT = "228D42"

bigdata = Bigdata()
search = bigdata.search.new(query=(Entity(MICROSOFT) | Keyword("tesla")))
comentions = search.get_comentions()
comentions_dic = comentions.to_dict()
print(comentions)