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COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries 被引量:5
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作者 Saleh Albahli Ahmad Algsham +5 位作者 Shamsulhaq Aeraj muath Alsaeed muath alrashed Hafiz Tayyab Rauf Muhammad Arif Mazin Abed Mohammed 《Computers, Materials & Continua》 SCIE EI 2021年第5期1613-1627,共15页
Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brough... Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms.This phenomenon caused a state of panic among people.Different studies were conducted to stop the spread of fake news to help people cope with the situation.In this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)techniques.It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative reactions.The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment overall.The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly. 展开更多
关键词 COVID-19 sentiment analysis natural language processing TWITTER social data mining sentiment polarity
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