Using Word Clouds and Word Frequency to Preview the COVID-19 Situation in Thailand through English Online News
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Abstract
Word Clouds means graphic visualization which is used to summarize textual data by showing frequent keywords occurring in the text in different sizes and colors. Recent studies have used word clouds and word frequency to summarize a large collection of data. Our paper, therefore, aims to investigate whether word clouds and word frequency could be utilized for previewing data related to the COVID-19 through English online news from the Bangkok Post. In this study, there were 303 English online news, consisting of 198,685 words in order to see the first wave of pandemic situation. The data were prepared before being processed. After that, the data were then processed by using Word sift. The results of the study present twelve-word clouds, and top-ten keywords in each month. The results obtained from descriptive analysis were utilized to see the how word clouds and word frequency connect to the first wave of the COVID-19 pandemic situation in Thailand in 2020. Our results support the use of word clouds and word frequency for previewing large data collection. However, this study also presents some problematic issues, for example, data preparation, the appropriate number of keywords used in word clouds, and the qualitative data should not be solely relied on using word clouds. The results of the study report the discussion and provide guidance on how to use word clouds together with other research approaches for analyzing qualitative data.
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This article is published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which allows others to share the article with proper attribution to the authors and prohibits commercial use or modification. For any other reuse or republication, permission from the journal and the authors is required.References
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