An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis
| Autor: | Sarmini, Abdullah Alhabeeb, Majed Mohammed Abusharhah, Taqwa Hariguna, Andhika Rafi Hananto |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | JOIV: International Journal on Informatics Visualization, Vol 6, Iss 3, Pp 604-609 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2549-9610 2549-9904 |
| DOI: | 10.30630/joiv.6.3.894 |
| Popis: | An anti-Covid-19 plan with social restrictions forced all Indonesian educational institutions to implement online learning in 2020. Strategy in early 2022, a new policy brought back online learning methods. Because of the rapid change and short adaptation period, online learning, which had been accepted as a solution for approximately two years, has become controversial. There were a variety of reactions in society, particularly on social media, after the rapid shift from face-to-face learning to online learning. This study will quantify text sentiment expressed on social media through machine learning. This study used SVM, RF, DT, LR, and k-nearest neighbors to develop a sentiment analysis model for use in sentiment research (KNN). The SVM- and RF-based sentiment analysis models outperform the others in cross-validation tests using data from the same Twitter social media site. Furthermore, RF can classify public opinion into three groups: positive, negative, and neutral, with a low error rate. The f1 values of our KNN-based model were measured at 75%, 65%, and 87% for negative, neutral, and positive tweets, respectively, which are slightly more accurate than previous studies with the same method and purpose. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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