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Detecting User Emotions in Twitter through Collective Classification

Karagöz, Pınar
The explosion in the use of social networks has generated a big amount of data including user opinions about varying subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this study, a different user-centric approach for emotion detection is considered such that connected users may be more likely to hold similar emotions; therefore, leveraging relationship information can complement emotion inference task in social networks. Employing Twitter as a source for experimental data and working with the proposed collective classification algorithm, emotions of the users are predicted in a collaborative setting.