TY - GEN
T1 - Emotion detection from text via ensemble classification using word embeddings
AU - Herzig, Jonathan
AU - Shmueli-Scheuer, Michal
AU - Konopnicki, David
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Emotion detection from text has become a popular task due to the key role of emotions in human-machine interaction. Current approaches represent text as a sparse bag-of-words vector. In this work, we propose a new approach that utilizes pre-trained, dense word embedding representations. We introduce an ensemble approach combining both sparse and dense representations. Our experiments include five datasets for emotion detection from different domains and show an average improvement of 11.6% in macro average F1-score.
AB - Emotion detection from text has become a popular task due to the key role of emotions in human-machine interaction. Current approaches represent text as a sparse bag-of-words vector. In this work, we propose a new approach that utilizes pre-trained, dense word embedding representations. We introduce an ensemble approach combining both sparse and dense representations. Our experiments include five datasets for emotion detection from different domains and show an average improvement of 11.6% in macro average F1-score.
UR - http://www.scopus.com/inward/record.url?scp=85033227742&partnerID=8YFLogxK
U2 - 10.1145/3121050.3121093
DO - 10.1145/3121050.3121093
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AN - SCOPUS:85033227742
T3 - ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
SP - 269
EP - 272
BT - ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017
Y2 - 1 October 2017 through 4 October 2017
ER -