EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning

Hani Al-Omari, Malak Abdullah, Nabeel Bassam

Abstract
Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).
Anthology ID:
S19-2032
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
200–204
Language:
URL:
https://aclanthology.org/S19-2032
DOI:
10.18653/v1/S19-2032
Bibkey:
Cite (ACL):
Hani Al-Omari, Malak Abdullah, and Nabeel Bassam. 2019. EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 200–204, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning (Al-Omari et al., SemEval 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/S19-2032.pdf
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EmoContext
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