HJ-Biplot as a Tool to Give an Extra Analytical Boost for the Latent Dirichlet Assignment (LDA) Model: With an Application to Digital News Analysis about COVID-19

Abstract

This work objective is to generate an HJ-biplot representation for the content analysis obtained by latent Dirichlet assignment (LDA) of the headlines of three Spanish newspapers in their web versions referring to the topic of the pandemic caused by the SARS-CoV-2 virus (COVID-19) with more than 500 million affected and almost six million deaths to date. The HJ-biplot is used to give an extra analytical boost to the model, it is an easy-to-interpret multivariate technique which does not require in-depth knowledge of statistics, allows capturing the relationship between the topics about the COVID-19 news and the three digital newspapers, and it compares them with LDAvis and heatmap representations, the HJ-biplot provides a better representation and visualization, allowing us to analyze the relationship between each newspaper analyzed (column markers represented by vectors) and the 14 topics obtained from the LDA model (row markers represented by points) represented in the plane with the greatest informative capacity. It is concluded that the newspapers El Mundo and 20 M present greater homogeneity between the topics published during the pandemic, while El País presents topics that are less related to the other two newspapers, highlighting topics such as t_12 (Government_Madrid) and t_13 (Government_millions).