COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

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

[1]  Haoyu Wang,et al.  Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter , 2020, 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[2]  Emilio Ferrara,et al.  What types of COVID-19 conspiracies are populated by Twitter bots? , 2020, First Monday.

[3]  Emily K. Vraga,et al.  A first look at COVID-19 information and misinformation sharing on Twitter , 2020, ArXiv.

[4]  Sungyong Seo,et al.  COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations , 2020 .

[5]  David G. Rand,et al.  Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention , 2020, Psychological science.

[6]  Heidi Larson,et al.  The pandemic of social media panic travels faster than the COVID-19 outbreak , 2020, Journal of travel medicine.

[7]  H. Fu,et al.  Mental health problems and social media exposure during COVID-19 outbreak , 2020, PloS one.

[8]  El Moatez Billah Nagoudi,et al.  AraNet: A Deep Learning Toolkit for Arabic Social Media , 2019, OSACT.

[9]  Haoyu Wang,et al.  Task Failure Prediction in Cloud Data Centers Using Deep Learning , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[10]  Abed Allah Khamaiseh,et al.  A comprehensive survey of arabic sentiment analysis , 2019, Inf. Process. Manag..

[11]  Dirk Burghardt,et al.  Analyzing and Visualizing Emotional Reactions Expressed by Emojis in Location-Based Social Media , 2019, ISPRS Int. J. Geo Inf..

[12]  Siddharth Swarup Rautaray,et al.  Adaptive Model for Sentiment Analysis of Social Media Data Using Deep Learning , 2019, ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management.

[13]  Ahmed Z. Emam,et al.  Sentiment Analysis of Saudi Dialect Using Deep Learning Techniques , 2019, 2019 International Conference on Electronics, Information, and Communication (ICEIC).

[14]  Serkan Ayvaz,et al.  Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis , 2018, Telematics Informatics.