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 lexicons .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 implemented in this study, and the validation accuracies for Naive Bayes model and Recurrent Neural Network model are found to be .548 and .812.
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