E-LEARNING TWITTER SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE: A COVID-19 CASE STUDY - Zuhri Arafah Zulkifli; Norsabrina Aznan (Journal of Islamic, Social, Economics and Development, 2022)
It has been more than a year since the Covid-19 pandemic struck. It is evident thatadoption of online teaching has changed the landscape of the education sector. However, theefficacy of online teaching is the main concern because it depends on the study of the presenttime as the method is evolving. Another problem is platform such as Sistem Pengurusan AduanAwam (SISPAA) that exist to accept complaints and suggestions from the people is found to bevery inefficient since not many people are familiar with it. Furthermore, it is quite complex tointerpret the sentences and analysing the sentiments due to the granularities of words andphrases. As a solution, Sentiment Analysis can be conducted to analyse the sentiments of thepeople on Twitter platform. These sentiments will reflect the view and the efficacy of the newmethod by providing statistical analysis on the issue. Lexicon and rule-based tool, VADER isused to label the sentiments that later will be visualized, and further steps are taken to test theaccuracy of Support Vector Machine (SVM) model against Linear Regression (LR) model. Theanalysis shows majority of the twitter users have a positive review for E-learning and the SVMis the better classifier because it has a higher accuracy percentage compared to LR. Thisproject follows Data Science Life Cycle methodology and confusion matrix is used to visualizethe accuracy testing for both models. The significance of this project is to provide a statisticalanalysis on the new approach based on Twitter data. Plus, the findings of this project can beused to improve the implementation of current online teaching method. For future research,Sentiment Analysis can be improved by tuning different parameter property and using anothervarious technique with different machine learning models.