Sentiment Analysis of Urban Opinions on COVID-19 Handling in Brunei Darussalam Using Lexicon Weighting and Machine Learning Algorithms

Usman Ependi, Wahyu Caesarendra

Abstract


Crisis management of Covid-19 is closely related to how government provides policy measures and monitors the health conditions of residents and others. Residents will provide feedback (opinions) for any services provided by the government. The main issue in this area is understanding residents' opinions to become a source of information for sentiment in public policy. This study aims to analyze sentiment on crisis management of covid-19 in Brunei Darussalam. Lexicon weight and machine learning classifiers (random forest, k-nearest neighbors, naive Bayes, and decision trees) are used for handling this issue. The data used in this study comes from resident opinions on the BruHealth application, which is part of Brunei Darussalam Government Services. Based on the experimental results, the sentiment of crisis management of Covid-19 is positive. Lexicon weight is used as a basis for data labelling in machine learning classification. Classification results using random forest, k-nearest neighbors, naive Bayes, and decision trees get a significant accuracy of 83,8%, 73,7%, 55%, and 84,2%, respectively.

Keywords


sentiment analysis; covid-19; lexicon; machine learning

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DOI: https://doi.org/10.24167/sisforma.v11i1.11957

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