EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets

Mohammed Jabreel, Antonio Moreno


Abstract
This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regressor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task.
Anthology ID:
S18-1029
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
193–199
Language:
URL:
https://aclanthology.org/S18-1029
DOI:
10.18653/v1/S18-1029
Bibkey:
Cite (ACL):
Mohammed Jabreel and Antonio Moreno. 2018. EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 193–199, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets (Jabreel & Moreno, SemEval 2018)
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PDF:
https://aclanthology.org/S18-1029.pdf