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Cognitive Politics
US ELECTIONS 2016
Cognitive News Analytics to predict the Next US president
Sankar Nagarajan, TEXTIENT ...
Cognitive Analytics
• During the first two analysis , cognitive analytics was done on the
TEXTIENT platform powered by IBM...
Data & Intelligence
Data ?
• Sampling and Aggregation of hundreds of Public News
articles (text content)
1. Analysis term  “Hillary Clinton”
Topics discovered Dominant Entities discovered
News Location context
1. Analysis term  “Clinton”
Topics discovered Dominant Entities discovered
News Location context
1. Analysis term  “White House”
Topics discovered Dominant Entities discovered
News Location context
1. Analysis term  “US Election”
Topics discovered Dominant Entity discovered
News Location context
1. Analysis term  “Barack Obama”
Topics discovered Dominant Entities discovered
News Location context
1. Analysis term  “Presidential”
Topics discovered Dominant Entities discovered
News Location context
1. Analysis term  “Trump”
Topics discovered Dominant Entities discovered
News Location context
The Dominant Indicator
US Election 2016 Prediction
The Next US president is “Mr.Donald Trump”
Caution: The above view is not deterministic. Anyth...
THANK YOU
http://www.textient.com
Twitter: @_textient
Photo credits
Wikimedia Commons
Commons.Wikimedia.org
Cognitive Anal...
Disclaimer
• The views mentioned here are our own
• Data if used for this article have been sourced from available informa...
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Cognitive Politics US elections'16 closing predictions

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Final phase of Cognitive Analytics in predicting the US elections' 2016 outcome.

Published in: Data & Analytics
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Cognitive Politics US elections'16 closing predictions

  1. 1. Cognitive Politics US ELECTIONS 2016 Cognitive News Analytics to predict the Next US president Sankar Nagarajan, TEXTIENT Analytics 09, Nov 2016
  2. 2. Cognitive Analytics • During the first two analysis , cognitive analytics was done on the TEXTIENT platform powered by IBM Watson. You can see the details in our blog https://cognitivepoliticsblog.wordpress.com/ • This time for the final analysis, Given the juncture of the US elections (9, Nov’16) and the progress of the voting so far, I took a slightly different approach to the analysis where I directly used aggregated news content intelligence using IBM Watson to provide cues on who is likely to be elected and here it goes..!
  3. 3. Data & Intelligence Data ? • Sampling and Aggregation of hundreds of Public News articles (text content)
  4. 4. 1. Analysis term  “Hillary Clinton” Topics discovered Dominant Entities discovered News Location context
  5. 5. 1. Analysis term  “Clinton” Topics discovered Dominant Entities discovered News Location context
  6. 6. 1. Analysis term  “White House” Topics discovered Dominant Entities discovered News Location context
  7. 7. 1. Analysis term  “US Election” Topics discovered Dominant Entity discovered News Location context
  8. 8. 1. Analysis term  “Barack Obama” Topics discovered Dominant Entities discovered News Location context
  9. 9. 1. Analysis term  “Presidential” Topics discovered Dominant Entities discovered News Location context
  10. 10. 1. Analysis term  “Trump” Topics discovered Dominant Entities discovered News Location context
  11. 11. The Dominant Indicator
  12. 12. US Election 2016 Prediction The Next US president is “Mr.Donald Trump” Caution: The above view is not deterministic. Anything could change from my time of analysis till the election closing day as there are a number of people centric and external variables in play. https://cognitivepoliticsblog.wordpress.com You can read the analysis details at
  13. 13. THANK YOU http://www.textient.com Twitter: @_textient Photo credits Wikimedia Commons Commons.Wikimedia.org Cognitive Analytics by TEXTIENT using IBM Watson
  14. 14. Disclaimer • The views mentioned here are our own • Data if used for this article have been sourced from available information in the public domain and has not been authenticated by any statutory authority. • Although every reasonable effort is made to present current or appropriate information, there are no guarantees of any kind. Data accuracy cannot be guaranteed. All analysis included herein are based on data from public sources, but no representation or warranty, expressed or implied, is made as to their accuracy, completeness, timeliness, or correctness. We are not liable for any errors or inaccuracies, regardless of cause to you (readers, users).

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