SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning

Zinedine Rebiai, Simon Andersen, Antoine Debrenne, Victor Lafargue


Abstract
In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset.
Anthology ID:
S19-2051
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:
297–301
Language:
URL:
https://aclanthology.org/S19-2051
DOI:
10.18653/v1/S19-2051
Bibkey:
Cite (ACL):
Zinedine Rebiai, Simon Andersen, Antoine Debrenne, and Victor Lafargue. 2019. SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 297–301, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning (Rebiai et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2051.pdf