Examining logistics developments in post-pandemic Japan through sentiment analysis of Twitter data
Enna Hirata, Takuma Matsuda · Asian Transport Studies · 2023
The objective of this study is to utilize natural language processing technologies to examine data gathered from Twitter related to logistics in Japan during the COVID-19 pandemic. The Bidirectional Encoder Representations from Transformers (BERT) machine learning model is utilized to assess the sentiment of the content. The findings suggest a positive outlook on logistics during time frame analyzed. This research has four key implications: (1) the sentiment towards the term "logistics" is generally positive as per our analysis; (2) there is a trend of increasing interest in logistics in western Japan in 2022; (3) social media can be utilized as a tool to address the challenges faced by the logistics industry; and (4) our research highlights the potential of using social media data to provide a more timely and comprehensive analysis of logistics and transportation trends.