Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

Yung Chun Chang, Chih Hao Ku, Duy Duc Le Nguyen

研究成果: 雜誌貢獻文章同行評審

摘要

This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature.

原文英語
文章編號103587
期刊Information and Management
59
發行號2
DOIs
出版狀態已發佈 - 3月 2022

ASJC Scopus subject areas

  • 管理資訊系統
  • 資訊系統
  • 資訊系統與管理

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