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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2051.pdf