Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-...

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Autores principales:	Nora Alturayeif, Hamzah Luqman
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	BERT COVID-19 tweets emotion detection sentiment analysis transformers Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/55aaa3a11c4b45dcb34f2dae84e69944
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spelling	oai:doaj.org-article:55aaa3a11c4b45dcb34f2dae84e699442021-11-25T16:35:32ZFine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function10.3390/app1122106942076-3417https://doaj.org/article/55aaa3a11c4b45dcb34f2dae84e699442021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10694https://doaj.org/toc/2076-3417The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.Nora AlturayeifHamzah LuqmanMDPI AGarticleBERTCOVID-19 tweetsemotion detectionsentiment analysistransformersTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10694, p 10694 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	BERT COVID-19 tweets emotion detection sentiment analysis transformers Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	BERT COVID-19 tweets emotion detection sentiment analysis transformers Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Nora Alturayeif Hamzah Luqman Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function
description	The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.
format	article
author	Nora Alturayeif Hamzah Luqman
author_facet	Nora Alturayeif Hamzah Luqman
author_sort	Nora Alturayeif
title	Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function
title_short	Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function
title_full	Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function
title_fullStr	Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function
title_full_unstemmed	Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function
title_sort	fine-grained sentiment analysis of arabic covid-19 tweets using bert-based transformers and dynamically weighted loss function
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/55aaa3a11c4b45dcb34f2dae84e69944
work_keys_str_mv	AT noraalturayeif finegrainedsentimentanalysisofarabiccovid19tweetsusingbertbasedtransformersanddynamicallyweightedlossfunction AT hamzahluqman finegrainedsentimentanalysisofarabiccovid19tweetsusingbertbasedtransformersanddynamicallyweightedlossfunction
_version_	1718413075384631296

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

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