Sentiment Analysis on Acceptance of New Normal in COVID-19 Pandemic using Naïve Bayes Algorithm

• Published in: International journal of advanced computer science & applications 2022, Vol.13 (9)
• Main Authors: Samsudin, Siti Hajar Aishah, Sabri, Norlina Mohd, Isa, Norulhidayah, Bahrin, Ummu Fatihah Mohd
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Coronaviruses; COVID-19; Data mining; Disease transmission; Feature extraction; Pandemics; Sentiment analysis; Viral diseases
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.0130968

The COVID-19 pandemic has such a significant impact and causes difficulties in many aspects that the new normal rules should be implemented to reduce the effects. New normal rules have been implemented by governments worldwide to break the virus chain and stop its transmission among the society. Even if the COVID-19 outbreak is under control, governments still need to know whether society could adapt and adjust to their new daily lifestyles. Many precautions still must be addressed as the transition to endemic status does not mean that COVID-19 will naturally eventually disappear. The World Health Organization also has warned that it is too early to treat COVID-19 as an endemic disease. Since the pandemic, many interactions have been done online, leading to the increasing social media usage to express opinions about COVID-19. The objective of the study is to explore the capability of the Naïve Bayes algorithm in the sentiment classification of the public’s acceptance on the new normal in the COVID-19 pandemic. Naïve Bayes has been chosen for its good performance in solving various other classification problems. In this study, Twitter data were used for the analysis and were collected between March and June 2022. The evaluation results have shown that Naïve Bayes could generate excellent and acceptable performance in the classification with an accuracy of 83%. According to the findings of this research, many people have accepted the new normal in their daily lives. The future works would include scrapping more data based on geolocation, improving the feature extraction technique, balancing the dataset and comparing Naïve Bayes performance with other well-known classifiers. The subsequent study could also focus on detecting the emotions of the public and processing non-English tweets.