Real-time emotion classification of Tweets

Authors:
Olivier Janssens;Maarten Slembrouck;Steven Verstockt;Sofie Van Hoecke;Rik Van de Walle
Affiliations:
Ghent University, Ghent, Belgium;Ghent University, Ghent, Belgium;Ghent University, Ghent, Belgium;Ghent University, Ghent, Belgium;Ghent University, Ghent, Belgium
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

Despite adding emotions to applications has proven to enhance the user experience, emotion recognition applications are still not widely available nor used. Within this paper, emotion recognition is done on Twitter tweets using six emotion classification algorithms that are compared on precision and timing. The paper shows that precision can be enhanced by 5.02% compared to the current state-of-the-art by improving the features. Furthermore, the presented algorithms work in real-time.