How Pandemic Spread in News: Text Analysis Using Topic Model
Minghao Wang, Paolo Mengoni
TL;DR
This paper applies LDA topic modeling to analyze COVID-19 news articles and comments, revealing dominant themes and inconsistencies, and discusses potential improvements for future text analysis methods.
Contribution
It introduces a comprehensive LDA-based approach to analyze COVID-19 news and comments, including model tuning and topic interpretation, with insights into article-comment discrepancies.
Findings
Identified dominant COVID-19 topics in news and comments
Analyzed inconsistencies between articles and comments
Proposed three potential improvements for text analysis
Abstract
Researches about COVID-19 has increased largely, no matter in the biology field or the others. This research conducted a text analysis using LDA topic model. We firstly scraped totally 1127 articles and 5563 comments on SCMP covering COVID-19 from Jan 20 to May 19, then we trained the LDA model and tuned parameters based on the Cv coherence as the model evaluation method. With the optimal model, dominant topics, representative documents of each topic and the inconsistence between articles and comments are analyzed. 3 possible improvements are discussed at last.
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Taxonomy
TopicsComputational and Text Analysis Methods · Topic Modeling · Advanced Text Analysis Techniques
MethodsLinear Discriminant Analysis