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المؤلفون: Md. Mokhlesur Rahman, Jim Samuel, G. G. Md. Nawaz Ali, Ek Esawi, Yana Samuel
المصدر: Information, Vol 11, Iss 314, p 314 (2020)
Information
Volume 11
Issue 6مصطلحات موضوعية: FOS: Computer and information sciences, Coronavirus disease 2019 (COVID-19), bepress|Engineering, Computer science, PsyArXiv|Social and Behavioral Sciences|Social and Personality Psychology|Individual Differences, Context (language use), Machine learning, computer.software_genre, Computer Science - Information Retrieval, Naive Bayes classifier, Data visualization, Policy decision, Research article, information_technology_data_management, PsyArXiv|Engineering Psychology, PsyArXiv|Social and Behavioral Sciences|Social and Personality Psychology, Statistical software, Social and Information Networks (cs.SI), lcsh:T58.5-58.64, lcsh:Information technology, business.industry, Sentiment analysis, COVID-19, Computer Science - Social and Information Networks, Data science, textual analytics, Coronavirus, PsyArXiv|Social and Behavioral Sciences, Statistical classification, machine learning, Analytics, sentiment analysis, bepress|Social and Behavioral Sciences, bepress|Social and Behavioral Sciences|Psychology|Social Psychology, Classification methods, twitter, bepress|Social and Behavioral Sciences|Psychology|Personality and Social Contexts, Artificial intelligence, business, computer, Information Retrieval (cs.IR), Information Systems
الوصف: Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19&rsquo
s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naï
ve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.وصف الملف: application/pdf
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0f57367f3a99b8595d6630430953a8b