Abstract: This paper describes a Naive-Bayesian predictive model for 2016 U.S. Presidential Election based on Twitter data. We use 33,708 tweets gathered since December 16, 2015 until February 29, 2016. We introduce a simpler data preprocessing method to label the data and train the model. The model achieves 95.8% accuracy on 10-fold cross validation and predicts Ted Cruz and Bernie Sanders as Republican and Democratic nominee respectively. It achieves a comparable result to those in its competitor methods.

Introduction: Presidential election is an important moment for every country, including the United States. Their economic policies, which are set by the government, affect the economy of other countries BIBREF0 . On 2016 U.S. Presidential Election, Republican and Democratic candidates use Twitter as their campaign media. Previous researches have predicted the outcome of U.S. presidential election using Twitter BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . Some of them proved that Twitter data can complement or even predict the poll results. This follows the increasing improvement in the text mining researches BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 . Some of the most recent studies are BIBREF3 , BIBREF2 , BIBREF1 , BIBREF10 . Below we discuss these three recent studies and explain how our study relates to theirs. The first study is done by BIBREF3 , which analyzed the sentiment on 2008 U.S. Presidential Candidates by calculating sentiment ratio using moving average. They counted the sentiment value for Obama and McCain based on number of the positive and negative words stated on each tweet. The tweets were gathered during 2008-2009, whereas the positive and negative words were acquired from OpinionFinder. They found that the comparison between sentiment on tweets and polls were complex since people might choose "Obama", "McCain", "have not decided", "not going to vote", or any independent candidate on the polls. The second study predicted the outcome of 2012 U.S. Presidential Election polls using Naive Bayesian models BIBREF2 . They collected over 32 million tweets from September 29 until November 16, 2012. They used Tweepy and set keywords for each candidate to collect the tweets, such as mitt romney, barack obama, us election. The collected tweets passed some preprocessing stages: (1) URL, mentions, hashtags, RT, and stop words removal; (2) tokenization; and (3) additional not_ for negation. They analyzed 10,000 randomly selected tweets which only contain a candidate name. The analysis results were compared to Huffington Post's polls and they found that Obama's popularity on Twitter represented the polls result. This research didn't use tweets with two or more candidate names since it requires more complex preprocessing methods. The third study built a system for real-time sentiment analysis on 2012 U.S. Presidential Election to show public opinion about each candidate on Twitter BIBREF1 . They collected tweets for each candidates using Gnip Power Track since October 12, 2012 and tokenized them. The tweets were labeled by around 800 turkers on Amazon Mechanical Turk (AMT). They trained a Naive Bayes Classifier using 17,000 tweets which consists of 4 classes: (1) positive; (2) negative; (3) neutral; and (4) unsure. It achieved 59% accuracy, which is the best performance achieved in the three recent studies. They visualized the sentiment on a dashboard and calculated the trending words using TF-IDF. As far as we know, there is not any research about prediction on 2016 U.S. Presidential Election yet. Previous researches either set the sentiment of a tweet directly based on a subjectivity lexicon BIBREF3 or preprocessed the tweet using a complex preprocessing method BIBREF1 , BIBREF2 . BIBREF2 not only removed URLs, mentions, retweets, hashtags, numbers and stop words; but also tokenized the tweets and added not_ on negative words. BIBREF1 tokenized the tweets and separated URLs, emoticons, phone numbers, HTML tags, mentions, hashtags, fraction or decimals, and symbol or Unicode character repetition. This research analyzes sentiment on tweets about 2016 U.S. Presidential candidates. We will build a Naive Bayesian predictive model for each candidate and compare the prediction with RealClearPolitics.com. We expect to have a correct prediction on the leading candidates for Democratic and Republican Party. We prove that using a simpler preprocessing method can still have comparable performance to the best performing recent study BIBREF1 . We explain our data preparation methods in the next section. It is followed by our research methodology in Section III. We present our results in Section IV, which is followed by discussion and conclusion in Section V and VI.

Question: what preprocessing method is introduced?
Answer: Tweets without candidate names are removed, URLs and pictures are removed from the tweets that remain.