1. TITLE: Detection and Polarization of Political Sentiments on Twitter
By: Jamilu Awwalu
Prof. Azuraliza Abubakar
Dr Ridzwan Yaakub
Computer Science Department, BAZE University, Nigeria.
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

2. Introduction Social network services vary
location sharing, ideas, opinions, and event sharing
There is a huge amount of data that is posted daily by millions of users

3. RESEARCH Arguments
Humans have limitation on data mining data by just using the natural way of thinking or their common sense.
Majority of twitter messages are “pointless babble” Skemp (2009) & Pearanalytics (2009).
Purpose: Research and weight the arguments in the context of the political sentiments in a view to proving either of arguments as a valid statement and the possible reasons for its validity.

4. Research Instruments -
Language: R Programming Language
Data: US 2008 Presidential Debate Dataset. Collected by Nicholas and David (2010), and deposited on Infochimps Big Data Marketplace for free access by David Ayman

5. Research approach
The use of lexicon package or Bag of Words is one of the means of conducting sentiment analysis
Minqing Hu and Bing Liu (2004) opinion-lexicon- English

6. Research approach
Figure 1. Approach

7. Implementation
Loaded and scored based on positive and negative words from the lexicon
Score
Tweets
we dont need to keep hyping this fake war on terrorkeep us secure
how many times is obama going to have to say thats not true -1
wtf obama believes in missile defense since when debate current
debate currentquestion is a big softball for obama to whine about iraq some more
is obama stuttering nowcurrent
obama has to tell mccain thats not true john to wake him up he is sleeping during the debate
gorekerryobamaits a harvard dna thingthey all become androids current debate
current debate obama talks in circles about nothing current debate
did obamas mom not teach him any manners wait your turn to speak current
mccain is getting very angry
poor grades across the board in a recent study of preparednessgoodobamatalk about what were not doing yetcurrent debate

8. Implementation Sentiment Score for both candidates Score Text
current tweetdebate mccainshot stop interrupting obama mccain was just about to say my friends
Obama 1
i really would like to see obamamccain actually engage each other
McCain
tweetdebateobama for not answering earmark claim by mccain
tweetdebate looks like mccain cant make eye contact with obama and respond directly to him dang there goes my friends again
tweetdebatemccain untasty comment about obama not knowing about household income and wealth
how long until mccain notes the amount of times obama voted present rather than yea or nay tweetdebate -1
obama for breaking the debate rules and walking over the moderator tweetdebate
tweetdebate obama right to focus on issues in deregulation

9. Implementation
Positive Sentiment Plot

10. implementation
Negative Sentiment Plot

11. Result - US 2008 Election Result
Presidential Candidate
Vice Presidential Candidate
Political Party
Popular Vote
Electoral Vote
Barrak H. Obama
Joseph R. Biden, Jr.
Democratic
69,499,428
58.86%
365
67.8%
John S. McCain III
Sarah H. Palin
Republican
59,950,323
45.60%
173
32.2%
Ralph Nader
Matt Gonzalez
Independent
739,278
0.56%
0.0%
Bob Barr
Wayne Alyn Root
Libertarian
523,433
0.40%
Other -
761,243
0.58%
Total
131,473,705
538

12. Conclusion
This paper presented an approach for detection and scoring of political sentiments on social media. Obama and McCain are the entities whose sentiments were classified. Result of the classification shows candidate Obama to be ahead of McCain on positive sentiment scores, while candidate McCain was ahead of Obama on negative scores. This directly relates to the real life election result as Obama was announced to be the winner of the 2008 United States presidential election. This concludes that social media sentiments on politics if detected and correctly classified as in this study, election results can be predicted.