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공공누리This item is licensed Korea Open Government License

Title
Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives
Author(s)
AlAgha, Iyad
Publisher
Korea Institute of Science and Technology Information
Publication Year
2021-03-30
Abstract
The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter. It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time. In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed. Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic. Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change. In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries. Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels. Our intention was to analyze how the sentiment differs between countries and in response to certain events. The performance of the topic model was assessed by being compared with other alternative topic modeling techniques. The topic coherence was measured for the different techniques while changing the number of topics. Results showed that the pooling by author before performing LDA significantly improved the produced topic models.
Keyword
COVID-19; Twitter; topic modeling; sentiment analysis; Latent Dirichlet Allocation; social media
Journal Title
Journal of Information Science Theory and Practice;Volume 9 Issue 1
ISSN
2287-4577
DOI
https://doi.org/10.1633/JISTaP.2021.9.1.3
Files in This Item:
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Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 9 - No. 1
Type
Serial
URI
https://repository.kisti.re.kr/handle/10580/16264
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