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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De Gruyter
2021
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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) |
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sentiment analysis machine learning arabic covid-19 online education emotions Science Q Electronic computers. Computer science QA75.5-76.95 |
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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 |
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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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1718371670968762368 |