Title :
Real-time emotion classification of tweets
Author :
Janssens, Olivier ; Slembrouck, Maarten ; Verstockt, Steven ; Van Hoecke, Sofie ; Van de Walle, Rik
Author_Institution :
Electron. & Inf. Technol. Lab., Ghent Univ., Ghent, Belgium
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.
Keywords :
emotion recognition; pattern classification; social networking (online); text analysis; Twitter tweets; emotion classification algorithms; emotion recognition; real-time emotion classification; Emotion recognition; Machine learning algorithms; Real-time systems; Support vector machines; Training; Twitter;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location :
Niagara Falls, ON