Working with Concepts

This guide explains how you can use concepts to both analyze and filter by the brands, products, topics and well known people that are mentioned in content.

Concepts and types

Concepts are entities stored with their name and type. They are extracted from the articles that are shared or engaged with on LinkedIn.

There is a vast number of concepts that can be recognized. To help you work with concepts they are organised into the following types:

Type Description Examples
business A business concept mentioned in the content. For example, roles, titles, types of businesses, and terms typically associated with doing business. E-commerce Chief_executive_officer Venture_capital Business-to-business Human_resources
city A city mentioned in the content. Chicago New_York_City Sydney Mumbai Dublin
company A company mentioned in the content. IBM Starbucks Siemens The_Walt_Disney_Company Google
country A country mentioned in the content. China Australia United_States France Japan
currency A currency or monetary concept mentioned in the content. Pound_sterling Indian_rupee Legal_tender United_States_dollar
education An educational institution or organization (such as a university or college), or an education term such as 'Mathematics'.' Stanford_University University_of_London Syracuse_University Master_of_Business_Administration Maharishi_University_of_Management
event Any type of event mentioned in the content New_Year's_Eve Nobel_Peace_Prize Brexit Academy_Awards Consumer_Electronics_Show
government An entity associated with government in the broadest sense, including terms related to government and politics, government agencies, and government departments. United_States_Department_of_the_Navy NASA European_Union United_States_Senate Executive_order
location A location mentioned in the content aside from a city or country. For example a US state. Asia Yosemite_National_Park Minnesota Middle_East New_York_Stock_Exchange
media A publication or media outlet mentioned in the content. Podcast Rock_Music NBC_News Financial_Times Vanity_Fair_(magazine)
misc A concept not classified as one of the types listed in this table, such as an idea, theory, or abstract concept. Canadians Dog Year Swedish_language Turkish_people
organization An organization mentioned in the content (not classified as one of the types listed in this table). International_Organization_for_Standardization World_Economic_Forum Institution_of_Civil_Engineers NATO Apache_Software_Foundation
person A well-known person mentioned in the content. Richard_Branson Tom_Brady Benjamin_Franklin Elon_Musk Michelle_Obama
product

A product mentioned in the content.

Note that the definition of a product is broad, including software products and services.

Skype Energy_Drink IOS Microsoft_PowerPoint Go_(game)
science An entity that falls within science in the broadest sense. For example, organic materials, animals, plants, biology, physics, medicine, and human sciences. DNA Climate_Change Space_Craft Hypertension Diabetes_mellitus
sport An entity that relates to sport, such as competitions, leagues, teams, and types of sport. Formula_One National_Football_League Marathon Chelsea_F.C. San_Jose_Sharks
technology A technology or technology concept mentioned in the content. Artificial_Intelligence Bluetooth Speech_recognition Computer_science Web_conferencing

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Currently concepts are only extracted from content written in English.

Analysis and filtering using concepts

There are three targets made available for filtering and analyzing concepts:

  • li.all.concepts.names - the names of concepts that have been extracted.
  • li.all.concepts.types - the types of concepts that have been extracted.
  • li.all.concepts.type_names - the name and type of concepts that have been extracted.

Each can be used for analysis or as part of query filters.

Analyzing concepts

Concepts are great for helping you to explore content that is popular on LinkedIn. They take you beyond trying to simply digest page titles and urls, and give you a view into the content of popular pages.

For most use cases the li.all.concepts.type_names target is most useful for analysis as it includes both the name and type of the concepts.

For example, the following analysis task analyzes the top concepts mentioned in content relating to machine learning:

{
  "subscription_id": "e9dde04774540ac119c2317a4d15a8b3a1350937",
  "name": "Machine learning concepts",
  "type": "analysis",
  "parameters": {
    "filter": "li.all.articles.title contains_any \"ai, artifical intelligence, ml, machine learning\"",
    "parameters": {
      "analysis_type": "freqDist",
      "parameters": {
        "target": "li.all.concepts.type_names",
        "threshold": 50
      }
    }
  }
}

Running the analysis gives results such as the following:

Type|Name Type Concept name Unique authors Interactions
technology|Deep_learning technology Deep_learning 62,100 95,700
person|Elon_Musk person Elon_Musk 50,000 59,400
organization|World_Economic_Forum organization World_Economic_Forum 43,500 50,200
education|Massachusetts_Institute_of_Technology education Massachusetts_Institute_of_Technology 24,300 37,200

The result tells us that 24,300 members engaged with content that mentioned MIT in the analysis period.

Filtering by concept mentions

You can also use concepts in your query filters. This enables you to filter not just on titles and summaries of content, but also on concepts mentioned in the body of content.

Taking the filter used in the example above for machine learning:

li.all.articles.title contains_any "ai, artifical intelligence, ml, machine learning"

This filter could be extended to look for concepts in the body of content:

li.all.articles.title contains_any "ai, artifical intelligence, ml, machine learning"
OR li.all.concepts.type_names in "technology|Deep_learning,technology|Artificial_intelligence,technology|Machine_learning"

This broadens the filter to include more content and is a great technique for improving your filters.

Filtering by concept type

Note that filtering by a concept type will not only return concepts of that type.

Take this example task:

{
  "subscription_id": "e9dde04774540ac119c2317a4d15a8b3a1350937",
  "name": "Company concepts",
  "type": "analysis",
  "parameters": {
    "filter": "li.all.concepts.types == \"company\"",
    "parameters": {
      "analysis_type": "freqDist",
      "parameters": {
        "target": "li.all.concepts.type_names",
        "threshold": 50
      }
    }
  }
}

This request is asking to analyze all concepts in content where at least one concept of type company is mentioned. It is not asking to analyze only concepts of type company.

Therefore the top entities that are mentioned in content alongside at least one company concept will be returned.

If you want to obtain a list of concepts of a certain type then:

  • use the li.all.concepts.type_names target for your analysis
  • in post-processing split the type and names like in the table of results above
  • filter to only the results that match the type you need