How often do you use your nose/face mask?

Next Article

What is your salary level?

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Taiwo Olapeju Olaleye1, Segun Michael Akintunde2 , Chidi Akparanta3 , Tijani Agidibo Avovome4 , Ojugbele Francis Oluyen45, Ayodele Olubunmi Akparanta6 1, 3, 6Computer Centre & Services, Federal College of Education, Abeokuta, Nigeria 2Computer Science Department, Federal University of Agriculture, Nigeria 4Medical Centre, Federal College of Education, Abeokuta, Nigeria 5Management Information System, Federal College of Education, Abeokuta, Nigeria

Abstract: Data has indisputably proven overtime to have a better idea and with the surge of big data in the era of coronavirus, research initiatives in the field of data mining continues to leverage computational methodologies. Owing to the dreadful nature of the Omicron-variant, a fight or flight dilemma readily pervades college communities far reaching implications on work ethics of academic front-liners. This study therefore aim to gain insights from academia-sourced data to unravel fear-stimulus in college communities. The predictive analytics is carried out on college-based opinion poll. The Valence Aware Dictionary for Sentiment Reasoning algorithm is deployed for emotion analytics while the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) are employed for topic modelling. The REPTree algorithm models the fear-spotting decision tree using 10-fold cross-validation. Experimental results shows a high performance metrics of 94.68% on Recall and Precision as the hand-washing attribute is returned as the most significant variable with highest information gain. Results of topic modelling likewise returns non-clinical precautionary measures as fear stimulus while the Vader sentiment analysis shows a 22.47%, 25.8%, and 51.73% positive, negative, and neutral polarity scores respectively, indicative of the academic front-liners’ pessimism towards effective safety measure compliance with non-clinical regulations. Keywords: COVID-19, Omicron, Sentiment Analysis, Topic Modelling.

I. INTRODUCTION

Besides the reality of the dreadful Omicron-variant prevalence, fear is another factor that continued to shape behaviour across world communities [1] as the entire universe grapples with the pandemic. The virus was declared as a Public Health Emergency of International Concern (PHEIC) on 30th January, 2020 and, later on 11th March, 2020 was characterized as a pandemic [2]. The COVID-19 pandemic, which is almost convulsing the entire planet, has rendered public health across nations vulnerable and calls for desperate measures to curtail the ugly trend. Proactive measures such as the unprecedented total lockdown of socio-economic activities to curtail the highly infectious and mobile virus was introduced worldwide running into several months. This particular precautionary measure is believed to have contributed immensely towards taming the tide of the ugly trend. However, mixed feelings of apprehension trails reopening of schools by the Nigerian government after the Yuletide break, due to the prevalence of the Omicron-variant. Moreover, the variant has been described as more dangerous than the previous waves. Actually, a more easily contagious Omicron-variant of the virus has raised concern of authorities and citizens alike owing to the recent upsurge of positive cases daily reported in the media. Fear of community spread of the variant has motivated government across levels to reemphasize the need to observe non-clinical measures including observing physical distance, avoiding gathering of more than 50 people, observance of the use face masks etc. besides the ongoing clamor for vaccination. Whereas, the Nigerian government stipulated certain policy measures as preventive measures to be adhered to its workers including school administrators, the knowledge of the fact that over 65% of the population are upwardly mobile youths of ages below 35 years [3] and some of whom are undergraduates in Tertiary Educational Institutions (TEIs) is indeed a source of concern. Consequently, teaching and non-teaching staff of TEIs who are mostly the vulnerable adults, are at the risk of contracting the virus; hence an expected level of apprehension across Nigeria’s universities and college campuses.