Public Perceptions of Online Learning in Developing Countries: A Study Using The ELK Stack for Sentiment Analysis on Twitter

Satria Fadil Persada, Andri Oktavianto, Bobby Miraja, Reny Nadlifatin, Prawira Fajarindra Belgiawan, A.A.N Perwira Perwira Redi

Abstract


This study explores public perceptions toward online learning application in Indonesia. Many studies about online learning were done in developed countries and only a few in developing countries. Moreover, these studies used a qualitative approach which limits the results to be applied in different settings. While traditional research using a survey to understand people's perception towards an entity requires a lot of time and efforts; we used efficient and effective manners to gather opinions and then analysed its sentiments using the Logstash, Kibana and Python programming language stack (ELK) stack and Naïve Bayes algorithm. We used Naïve Bayes algorithm for sentiment analysis and ELK stack for storing & gathering tweets from Twitter. With ELK stack, we successfully collected 133.477 tweets related to online learning. From this study, we understood what kind of words that are sentimentally positive and negative tweets. We also gained some insights regarding Indonesia’s student online learning application preferences.

Keywords


Online Learning; Sentiment analysis; Twitter; Developing Country; ELK stack

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Copyright (c) 2020 Andri Oktavianto, Satria Fadil Persada, Bobby Miraja, Reny Nadlifatin, Prawira Fajarindra Belgiawan, A.A.N Perwira Perwira Redi


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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