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http://dx.doi.org/10.6109/jkiice.2021.25.2.308

Tweets analysis using a Dynamic Topic Modeling : Focusing on the 2019 Koreas-US DMZ Summit  

Ko, EunJi (Division of Digital Media, Ewha Womans University)
Choi, SunYoung (Graduate School of Communication & Arts, YonSei University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.2, 2021 , pp. 308-313 More about this Journal
Abstract
In this study, tweets about the 2019 Koreas-US DMZ Summit were collected along with a time sequence and analyzed by a sequential topic modeling method, Dynamic Topic Modeling(DTM). In microblogging services such as Twitter, unstructured data that mixes news and an opinion about a single event occurs at the same time on a large scale, and information and reactions are produced in the same message format. Therefore, to grasp a topic trend, the contextual meaning can be found only by performing pattern analysis reflecting the characteristics of sequential data. As a result of calculating the DTM after obtaining the topic coherence score and evaluating the Latent Dirichlet Allocation(LDA), 30 topics related to news reports and opinions were derived, and the probability of occurrence of each topic and keywords were dynamically evolving. In conclusion, the study found that DTM is a suitable model for analyzing the trend of integrated topics in a specific event over time.
Keywords
Dynamic topic modeling(DTM); LDA; Topic coherence; Tweets; 2019 Koreas-US DMZ summit;
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