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Exploring Opinions on University Online Classes During the COVID-19 Pandemic Through Twitter Opinion Mining

  • Journal of the Korean Society for Library and Information Science
  • 2021, 55(4), pp.5-22
  • DOI : 10.4275/KSLIS.2021.55.4.005
  • Publisher : 한국문헌정보학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : October 17, 2021
  • Accepted : November 22, 2021
  • Published : November 30, 2021

Dong Hun Kim 1,  Ting Jiang 1,  Yong-Jun Zhu 2

1성균관대학교 문헌정보학과
2성균관대학교

Excellent Accredited

ABSTRACT

This study aimed to understand how people perceive the transition from offline to online classes at universities during the COVID-19 pandemic. To achieve the goal, we collected tweets related to online classes on Twitter and performed sentiment and time series topic analysis. We have the following findings. First, through the sentiment analysis, we found that there were more negative than positive opinions overall, but negative opinions had gradually decreased over time. Through exploring the monthly distribution of sentiment scores of tweets, we found that sentiment scores during the semesters were more widespread than the ones during the vacations. Therefore, more diverse emotions and opinions were showed during the semesters. Second, through time series topic analysis, we identified five main topics of positive tweets that include class environment and equipment, positive emotions, places of taking online classes, language class, and tests and assignments. The four main topics of negative tweets include time (class & break time), tests and assignments, negative emotions, and class environment and equipment. In addition, we examined the trends of public opinions on online classes by investigating the changes in topic composition over time through checking the proportions of representative keywords in each topic. Different from the existing studies of understanding public opinions on online classes, this study attempted to understand the overall opinions from tweet data using sentiment and time series topic analysis. The results of the study can be used to improve the quality of online classes in universities and help universities and instructors to design and offer better online classes.

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