Arabic sentiment analysis about online learning to mitigate covid-19

The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natura...

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Autor principal:	Ali Manal Mostafa
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	sentiment analysis machine learning arabic covid-19 online education emotions Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/65397c9cbafa4ea5b00febb645edf9d5
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id	oai:doaj.org-article:65397c9cbafa4ea5b00febb645edf9d5
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spelling	oai:doaj.org-article:65397c9cbafa4ea5b00febb645edf9d52021-12-05T14:10:51ZArabic sentiment analysis about online learning to mitigate covid-192191-026X10.1515/jisys-2020-0115https://doaj.org/article/65397c9cbafa4ea5b00febb645edf9d52021-04-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0115https://doaj.org/toc/2191-026XThe Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier.Ali Manal MostafaDe Gruyterarticlesentiment analysismachine learningarabiccovid-19online educationemotionsScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 524-540 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sentiment analysis machine learning arabic covid-19 online education emotions Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	sentiment analysis machine learning arabic covid-19 online education emotions Science Q Electronic computers. Computer science QA75.5-76.95 Ali Manal Mostafa Arabic sentiment analysis about online learning to mitigate covid-19
description	The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier.
format	article
author	Ali Manal Mostafa
author_facet	Ali Manal Mostafa
author_sort	Ali Manal Mostafa
title	Arabic sentiment analysis about online learning to mitigate covid-19
title_short	Arabic sentiment analysis about online learning to mitigate covid-19
title_full	Arabic sentiment analysis about online learning to mitigate covid-19
title_fullStr	Arabic sentiment analysis about online learning to mitigate covid-19
title_full_unstemmed	Arabic sentiment analysis about online learning to mitigate covid-19
title_sort	arabic sentiment analysis about online learning to mitigate covid-19
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/65397c9cbafa4ea5b00febb645edf9d5
work_keys_str_mv	AT alimanalmostafa arabicsentimentanalysisaboutonlinelearningtomitigatecovid19
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Arabic sentiment analysis about online learning to mitigate covid-19

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