ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets

El Moatez Billah Nagoudi


Abstract
This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN. This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets. ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets. Both models use a set of features including sentiment lexicon, negation, word embedding and emotion symbols features. Our system combines these features to assist the sentiment analysis task. ARB-SEN system achieves a correlation score of 0.720, ranking 6th among all participants in the valence intensity regression (V-reg) for the Arabic sub-task organized within the SemEval 2018 evaluation campaign.
Anthology ID:
S18-1055
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:
364–368
Language:
URL:
https://aclanthology.org/S18-1055
DOI:
10.18653/v1/S18-1055
Bibkey:
Cite (ACL):
El Moatez Billah Nagoudi. 2018. ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 364–368, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets (Nagoudi, SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1055.pdf