Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel
| Autor: | Ade Widyatama Dian, Pulung Hendro Prastyo, Adhistya Erna Permanasari, Amin Siddiq Sumi |
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| Rok vydání: | 2020 |
| Předmět: |
Decision support system
Government business.industry Computer science Perspective (graphical) Sentiment analysis Confusion matrix 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences Public opinion computer.software_genre 01 natural sciences Support vector machine Kernel (statistics) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | Journal of Information Systems Engineering and Business Intelligence; Vol. 6 No. 2 (2020): October; 112-122 |
| ISSN: | 2443-2555 2598-6333 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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