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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.

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Citation

Please use this url to cite or link to this publication:

Chicago
Janssens, Olivier, Maarten Slembrouck, Steven Verstockt, Sofie Van Hoecke, and Rik Van de Walle. 2013. “Real-time Emotion Classification of Tweets.” In 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 1430–1431.
APA
Janssens, O., Slembrouck, M., Verstockt, S., Van Hoecke, S., & Van de Walle, R. (2013). Real-time emotion classification of tweets. 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) (pp. 1430–1431). Presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
Vancouver
1.
Janssens O, Slembrouck M, Verstockt S, Van Hoecke S, Van de Walle R. Real-time emotion classification of tweets. 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM). 2013. p. 1430–1.
MLA
Janssens, Olivier, Maarten Slembrouck, Steven Verstockt, et al. “Real-time Emotion Classification of Tweets.” 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM). 2013. 1430–1431. Print.
@inproceedings{6855295,
  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.},
  author       = {Janssens, Olivier and Slembrouck, Maarten and Verstockt, Steven and Van Hoecke, Sofie and Van de Walle, Rik},
  booktitle    = {2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM)},
  isbn         = {9781450322409},
  language     = {eng},
  location     = {Niagara Falls, CANADA},
  pages        = {1430--1431},
  title        = {Real-time emotion classification of tweets},
  year         = {2013},
}

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