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, 22-25 November, NDC, Abuja, Nigeria

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

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.

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

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

Research approach Figure 1. Approach

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

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

Implementation Positive Sentiment Plot

implementation Negative Sentiment Plot

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

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.