Stacking BERT based Models for Arabic Sentiment Analysis

Hasna Chouikhi, Hamza Chniter, Fethi Jarray, Fethi Jarray

2021

Abstract

Recently, transformer-based models showed great success in sentiment analysis and were considered as the state-of-the-art model for various languages. However, the accuracy of Arabic sentiment analysis still needs improvements. In this work, we proposed a stacking architecture of Arabic sentiment analysis by combing different BERT models. We also create a large-scale dataset of Arabic sentiment analysis by merging small publicly available datasets. The experimental study proves the efficiency of the proposed approach in terms of classification accuracy compared to single model architecture.

Paper Citation

in Harvard Style

Chouikhi H., Chniter H. and Jarray F. (2021). Stacking BERT based Models for Arabic Sentiment Analysis. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD; ISBN 978-989-758-533-3, SciTePress, pages 144-150. DOI: 10.5220/0010648400003064

in Bibtex Style

@conference{keod21,
author={Hasna Chouikhi and Hamza Chniter and Fethi Jarray},
title={Stacking BERT based Models for Arabic Sentiment Analysis},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD},
year={2021},
pages={144-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010648400003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD
TI - Stacking BERT based Models for Arabic Sentiment Analysis
SN - 978-989-758-533-3
AU - Chouikhi H.
AU - Chniter H.
AU - Jarray F.
PY - 2021
SP - 144
EP - 150
DO - 10.5220/0010648400003064
PB - SciTePress