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EMOTION ANALYSIS OF TWEETS USING DEEP LEARNING TECHNIQUES

Microblogging is one of the most prominent forms of communication among internet users today. These services offer users a tool to share opinions on a plethora of issues and aspects of their lives, and hence, they are rich data sources for textual analysis, and offer vast potential to gain insight into the way people communicate. We examine the task of emotion analysis of twitter data and design a neural network architecture for the task of classifying each tweet into one of five emotion categories: joy, sadness, anger, laughter and fear. We also show a method to automatically collect and tag a corpus of data for emotion analysis. Our experiments showed that our models outperform current state of the art techniques for the proposed problem.