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

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.

Automated summary

This study investigates customer satisfaction using aspect-level sentiment analysis and visual analytics. It analyzes flight reviews on TripAdvisor to measure the impact of COVID-19 on passenger travel sentiment in different aspects. The study fills the research gap in the use of deep learning and word embedding techniques, particularly for aspect-level sentiment analysis. The findings contribute to the theoretical and managerial literature by complementing existing sentiment analysis methods and extending the use of data-driven and visual analytics approaches.