The document discusses methods of predicting the outcome of the 2016 U.S. presidential election using data analysis, social media sentiment, and user interactions. It highlights the distinction between raw data, processed information, knowledge, and understanding, while also noting the importance of text mining for classifying Twitter reactions towards candidates. Additionally, it includes various statistics related to social media usage during the election period and offers insights into online reactions toward candidates.

1. Can you predict
who will win the
US election?
2016 © CYBER GATES
Samvel Gevorgyan
CEO, CYBER GATES
MSc Cyber Security from C-DAC University

2. Who Dares Wins
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2016
2012

3. Who will win the 2016 U.S.
presidential election?
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Source: https://www.predictit.org/Market/1234/Who-will-win-the-2016-US-presidential-election

4. How good are you at predicting
election winners?
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Dell Buys EMC For $67B In
Largest Deal In Tech History
EMC, a company worth twice Dell's value.

5. What happens in an internet
minute?
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Think before you post online
what you are thinking and
feeling about the election
race.
Facebook
293,000 statuses are updated
31,25 million messages are sent
Twitter
440,640 new tweets go online, etc.
Sources:
 http://www.internetlivestats.com
 https://zephoria.com/top-15-valuable-facebook-
statistics/

6. Data analysis
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Data is Messy and Noisy
1.Data: raw data that has not been processed
for use.
2.Information: data that are processed to be
useful; provides answers to "who", "what",
"where", and "when" questions
3.Knowledge: application of data and
information; answers "how" questions
4.Understanding: appreciation of "why"
5.Wisdom: evaluated understanding.

7. Text Mining and Sentiment Analysis
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Source: http://www.enchantedlearning.com/wordlist/negativewords.shtml
Negative Words
A
abysmal
adverse
alarming
angry
annoy
anxious
apathy
appalling
atrocious
awful
B
bad
banal
barbed
belligerent
bemoan
beneath
boring
broken
Positive Words
A
absolutely
adorable
accepted
acclaimed
accomplish
accomplishment
achievement
action
active
admire
adventure
affirmative
affluent
agree
agreeable
amazing
angelic
appealing
approve

8. Text Mining and Sentiment Analysis
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positive
negative
The objective is to classify to tweets as Positive, Neutral, or Negative.

9. US presidential candidate Twitter
analysis
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Source: https://my.datameer.com/apps/US_Presidential_Candidates/details

10. Positive and negative reactions
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Assume you hate Donald Trump and you post some
negative thoughts about him online. Can we predict
your friends’ thoughts based on their likes or other
reactions?

11. Facebook Reactions:
anger vs laughs
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Source: http://vocativ.com

12. WWW.CYBERGATES.ORG
Source: http://vocativ.com
Facebook Reactions:
likes vs comments vs shares

13. Other JUICY information
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Metadata
 Genders
 Ages
 Locations
 Interests

14. Thank You!
 Corporate website
www.cybergates.org
 Company profile on Twitter
www.twitter.com/CyberGatesLLC
 Company fan page on Facebook
www.facebook.com/Cyber.Gates.page
 Company profile on LinkedIn
www.linkedin.com/company/CyberGates-LLC
 Company channel on Vimeo
www.vimeo.com/CyberGates
 Company channel on YouTube
www.youtube.com/TheCyberGates
Contacts