TY - GEN
T1 - Before and After COVID-19 Outbreak Using Variance Representation Comparative Analysis of Newspaper Articles on the Travel Hotel Industry
AU - Yang, Yeqing
AU - Asahi, Yumi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - This study explores the impact of COVID-19 on Japan’s travel industry by analyzing differences before and after the pandemic through articles from the Nihon Keizai Shimbun. It employs text mining techniques like Latent Semantic Analysis (LSA) and BERT (a natural language model) to process and categorize information from newspaper titles. The analysis involves extracting nouns using MeCab, creating a frequency matrix, decomposing it with NMF for clustering, and setting topics. BERT is used for text classification, focusing on token attention weights and variance representation. The data includes articles from Nikkei Morning News pre- and post-COVID-19, specifically tagged with “Travel & Hotel,” totaling 792 articles. Analysis revealed ten topics such as vaccines, business structures, and financial results. Hierarchical clustering grouped these topics across eight clusters. Findings indicate a shift in topics post-COVID-19 towards financial impacts and business activities, highlighting tokens related to company activities and keywords associated with the pandemic. Future work aims at improving classification accuracy and leveraging data insights.
AB - This study explores the impact of COVID-19 on Japan’s travel industry by analyzing differences before and after the pandemic through articles from the Nihon Keizai Shimbun. It employs text mining techniques like Latent Semantic Analysis (LSA) and BERT (a natural language model) to process and categorize information from newspaper titles. The analysis involves extracting nouns using MeCab, creating a frequency matrix, decomposing it with NMF for clustering, and setting topics. BERT is used for text classification, focusing on token attention weights and variance representation. The data includes articles from Nikkei Morning News pre- and post-COVID-19, specifically tagged with “Travel & Hotel,” totaling 792 articles. Analysis revealed ten topics such as vaccines, business structures, and financial results. Hierarchical clustering grouped these topics across eight clusters. Findings indicate a shift in topics post-COVID-19 towards financial impacts and business activities, highlighting tokens related to company activities and keywords associated with the pandemic. Future work aims at improving classification accuracy and leveraging data insights.
KW - BERT Text Classification
KW - COVID-19
KW - Travel Hotel Industry of Japan
UR - http://www.scopus.com/inward/record.url?scp=85196121234&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-60114-9_12
DO - 10.1007/978-3-031-60114-9_12
M3 - Conference contribution
AN - SCOPUS:85196121234
SN - 9783031601132
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 174
BT - Human Interface and the Management of Information - Thematic Area, HIMI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
A2 - Mori, Hirohiko
A2 - Asahi, Yumi
PB - Springer Science and Business Media Deutschland GmbH
T2 - Thematic Area Human Interface and the Management of Information, HIMI 2024, Held as Part of the 26th HCI International Conference, HCII 2024
Y2 - 29 June 2024 through 4 July 2024
ER -