Abstract
Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.
Highlights
The use of the Internet, especially the social media, is high in Indonesia
Result of Classification Model Based on Sentiment in The General Aspect The sentiment in the general aspects using three classes showed that the Support Vector Machine (SVM) algorithm was better than Multinomial Naïve Bayes (MNB)
The results have shown that SVM with Normalized Poly Kernel outperformed MNB both on data of the economic aspects and the general aspects categorized into two-classes and three-classes dataset
Summary
The use of the Internet, especially the social media, is high in Indonesia. Based on the results of Hootsuite Social Wear research released in January 2019, social media users in Indonesia reached 150 million or 56% of the total population, increasing by 20% from the previous survey[1]. The Indonesian government is considered slow in handling COVID-19 [4][5] and so the Indonesian government's policy in dealing with the COVID-19 outbreak became a trending topic on Twitter, evoking the pros and cons, especially because COVID-19 has been impacting many sectors severely. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter. Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Information Systems Engineering and Business Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
