Visualizing a Terror Group using Named Entity Tagging

We recently received a request to analyze news focusing on Lashkar-e-Taiba, an active militant terrorism organization located in South Asia. This is a simple task by using BrightPlanet’s REST API to export data from the Global News Data Feed.  The data load contains everything you need to perform a thorough analysis, including:

  • URL
  • web page title
  • document harvest date
  • full text of news article
  • extracted named entities: including crime, other threats, events, weapons, people, diseases, companies, countries, places, etc.
  • document and individual entity-level sentiment

The data below was visualized and shared using Tableau.

Named Entities Reveal Granular Patterns in Data

The named entities we’re focusing on in this dashboard are threats, events, weapons, and people. By default, we see the total count of entities over time. Between June 2015 and January 2018, there were about 3,800 news articles mentioning Lashkar-e-Taiba. Looking at the line chart, March 2016 and November 2017 jump out as months with increased activity. Clicking on any single month will filter all the data down to that month. Overall, we can see entities which frequently appear in this dataset include:

  • “attack”
  • “kill”
  • “terrorism”
  • “arrest
  • “India”
  • “Pakistan”
  • “Hafiz Saeed” (co-founder of Lashkar-e-Taiba)
Lashkar-e-Taiba news visualization

Click the image to go to interactive visualization

Matching Crimes to Locations with Entity Relationships

Knowing individual entities which appear in a document is helpful, but seeing how those entities relate to other nearby entities of interest can provide real insight. BrightPlanet uses Rosoka Series 6 as its entity tagging engine. One convenient feature of the software is automatically tagging relationships between nearby entities. The yellow dots on the map correspond to news articles containing a Crime-to-Location relationship. Hovering over the southern-most yellow dot on the map, we can see that the crime entity “theft” was found nearby the place entity of “North Paravoor”. If we jumped to that article from The New Indian Express, we would find the targeted sentence,

“Anoop, a native of North Paravoor, is the seventh accused in the case related to theft of ammonium nitrate, nitrate mixer and electric detonator from Thuruthiyil Traders, a shop functioning in Perumbavoor.”

These are just a few examples of the insights that can be found by adding structure to unstructured web content by tagging named entities. Please click into the interactive Tableau visualization to explore the nuggets of insight which can be found from a targeted search of news data.

Develop Business Insight through Unstructured Web Content with BrightPlanet

If you think your organization would benefit from trends and insights derived from access to a repository of news data with over 15 million articles from thousands of unique sources, check out BrightPlanet’s Global News Data Feed for a 30-day free trial.

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