NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning

Rolandos Alexandros Potamias, Georgios Siolas

Abstract
Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related data. However, capturing emotional states and the sentiment polarity of written excerpts requires knowledge on the events triggering them. Towards this goal, we present a computational end-to-end context-aware SA methodology, which was competed in the context of the SemEval-2019 / EmoContext task (Task 3). The proposed system is founded on the combination of two neural architectures, a deep recurrent neural network, structured by an attentive Bidirectional LSTM, and a deep dense network (DNN). The system achieved 0.745 micro f1-score, and ranked 26/165 (top 20%) teams among the official task submissions.
Anthology ID:
S19-2047
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
277–281
Language:
URL:
https://aclanthology.org/S19-2047
DOI:
10.18653/v1/S19-2047
Bibkey:
Cite (ACL):
Rolandos Alexandros Potamias and Georgios Siolas. 2019. NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 277–281, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning (Potamias & Siolas, SemEval 2019)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/S19-2047.pdf
Data
EmoContext
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