Analysis of Key Topics in Green Logistics Using LDA: Focusing on Keywords Before and After the COVID-19

Abstract

Since the COVID-19 pandemic, the trend of green logistics has been accelerating in the logistics industry. The purpose of this study is to analyze the changes in social interest in green logistics before and after the COVID-19 pandemic, based on keywords extracted from news articles. To this end, two periods were selected, pre-pandemic (2018-2019) and postpandemic (2020-2021). Text analysis and topic modeling were conducted for the news articles related to green logistics in each period. In order to collect the news articles, the Big Kinds database was used, and key topics for each period were derived by applying the Latent Dirichlet Allocation (LDA) technique, a topic modeling methodology. The derived key topics were further classified into subtopics such as storage, unloading, packaging, transportation/distribution, and corporate strategy/policy in relation to logistics by functions. As a result of the study, it was confirmed that in the post-pandemic period, interest in green logistics increased in the media, and there was a change in the tendency of use of specific keywords and topics. In addition, with the emergence of new topics related to green storage, it was suggested that the green storage field could become a new business model for green logistics.