JDS Journal of Data Science 1680-743X 1680-743X SOSRUC 160207 10.6339/JDS.201804_16(2).0007 Research Article Can Emoticons Be Used to Predict Sentiment? Cates Keenen Department of Mathematics, University of Evansville 1800 Lincoln Ave, Evansville, Indiana, 47722 USA Zhang Zeyu Department of Mathematics, University of Evansville 1800 Lincoln Ave, Evansville, Indiana, 47722 USA Xiao Pengcheng Department of Mathematics, University of Evansville 1800 Lincoln Ave, Evansville, Indiana, 47722 USA Dailey Calvin Department of Mathematics, University of Evansville 1800 Lincoln Ave, Evansville, Indiana, 47722 USA 16 2 355 376

Getting a machine to understand the meaning of language is a largely important goal to a wide variety of fields, from advertising to entertainment. In this work, we focus on Youtube comments from the top twohundred trending videos as a source of user text data. Previous Sentiment Analysis Models focus on using hand-labelled data or predetermined lexicon-s.Our goal is to train a model to label comment sentiment with emoticons by training on other user-generated comments containing emoticons. Naive Bayes and Recurrent Neural Network models are both investigated and im- plemented in this study, and the validation accuracies for Naive Bayes model and Recurrent Neural Network model are found to be .548 and .812.