DocumentCode
1827013
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
fYear
2013
fDate
25-28 Aug. 2013
Firstpage
1430
Lastpage
1431
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location
Niagara Falls, ON
Type
conf
Filename
6785892
Link To Document