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

Abstract

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.

Klasifikasi sentimen terhadap larangan pernikahan beda agama menggunakan metode Naive Bayes Classifier

“…Persamaan (4) menunjukkan bahwa c merupakan kelas atau label, dan x adalah vektor fitur yang dipakai untuk memprediksi kelas c. P(c|x) menunjukkan posterior probability suatu kelas yang diberikan fitur [20]. P(x|c) adalah likelihood, yaitu probability fitur x jika diketahui kelasnya c. P(c) adalah prior probability kelas, mewakili keyakinan awal tentang distribusi kelas.…”

Public Response to the Legalization of The Criminal Code Bill with Twitter Data Sentiment Analysis

“…The first research authors use one methodology for measurement sentiment analysis, shown in Table I. Authors in [10] have investigated the Naïve Bayes algorithm's capacity to classify public mood under COVID-19's new normal. From the 2807 tweets that have been processed, the test results show that Naïve Bayes has done an excellent job, with an accuracy of 83% and an F1score of 84%.…”

“…Based on previous research, researchers will use the Naïve Bayes [10][13] [14], SVM [11][12] [13][15] [16], and CART [15] in evaluating sentiment analysis. In addition, the N-gram and TF-IDF methods will be used because they are proven to increase accuracy [16].…”

“…It is simple to program the Naïve Bayes classification algorithm to perform exceptionally well in supervised learning, and it can also be used in difficult realworld situations. The Naïve Bayes method is simple to grasp, needs an education dataset to figure out how to calculate its variables, doesn't care about things that have nothing to do with the problem, and works well with correct data from a single source [25] [10].…”

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

“…However, this industry is starting to bloom again with the lifting of all the Covid-19 restrictions and also the less severe effects of virus infections. In the early days of the opening of tourism, people have been educated about the new normal procedures such as wearing masks, social distancing and sanitizing hands [2]. Since then, people have not been afraid to go anywhere in the world for a holiday getaway.…”