Election Tracker ’16: Transforming Unstructured Data into Interactive Insights

I’d like to share this awesome public-facing example of the stuff I’m working on now.
Check out Election Tracker ’16,  a new website where you can visually monitor, compare, and discover interesting facts about the 2016 US Presidential Election coverage from the world’s top 48 online news agencies.


I’m a Product Manager for OpenText InfoFusion, the software in Election Tracker ’16 that is collecting and processing all the news articles.  After it has collected everything, InfoFusion translates the free text into specific topics, entities and concepts as well performing a sentiment analysis on the unstructured data using both statistical and linguistic analysis.  OpenText iHub software is then used for the data visualization and embedded analytics you see on the website.


election tracker 16 behind the scenes 1

Election Tracker is great example of the power of InfoFusion + iHub, but it’s only a single use case.

You could apply this system to any huge collection of unstructured data that potentially holds valuable insights and knowledge.  Once you modify and tune the content analytic system’s taxonomy and linguistic framework with details and language specific to a particular knowledge area (e.g., aerospace industry, neuroscience research, general business), you could then point it to your collection of unstructured data like research papers, medical reports, support tickets, lab notebooks, legal documents, email, twitter feeds, etc.

Some user stories off the top of my head: Market analysts who want to evaluate social media content to understand customer sentiment.  Legal analysts who need a quick understanding of context and sentiment in large volumes of legal briefs.  Pharmaceutical companies who want to be aware of potential breakthrough discoveries that are only apparent when the contents of all their research lab notebooks are conceptually organized and correlated.

More focused on healthcare, I could imagine healthcare organizations using this technology as a window into all those data that never make it into the structured format of a medical record.  One could look for insights into unreported drug interactions and outcomes by tracking particular medical procedures and medications and the sentiments around them as expressed in the large volume of unstructured notes attached to medical records.  And in academic healthcare, imagine using a system like this to get a big-picture view of research trends and patterns across large numbers of peer reviewed journals in a particular field.

Imagine the possibilities!

election tracker 16 behind the scenes 2


Take care,
– John Lester
Product Manager | OpenText | InfoFusion