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Lathifah Alfat
Universitas Pembangunan Jaya

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Web mining and sentiment analysis of COVID-19 discourse in online forum communities Masurah Mohamad; Suraya Masrom; Khairulliza Ahmad Salleh; Lathifah Alfat; Muhammad Nasucha; Nur Uddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1280-1287

Abstract

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
Co-Authors Khairulliza Ahmad Salleh Masurah Mohamad Muhammad Nasucha Nur Uddin Suraya Masrom