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This study investigated tourist satisfaction with Singaporean hotels using online Airbnb reviews from 2011 to 2024. Sentiment analysis was performed on this digital word-of-mouth data. This study proposed a BERT-BiLSTM-Attention model to train and test on the public TripAdvisor dataset, employing Python libraries like SpaCy and Matplotlib for aspect extraction and data visualization. The analysis identified key aspects (hotel, room, staff, service, food) and revealed a predominantly positive outlook (95% positive reviews). This research contributes to the literature on using text mining and offers practical insights for enhancing service quality in the hotel sector by understanding tourist preferences.

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Analyzing Online Word-of-Mouth in Tourism Using Text Mining

  • 会议论文

  • 发表日期：

    2026年

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  • Hoai-Phuong Nguyen-Cao,
  • Ho Nhu Ngoc

摘要

This study investigated tourist satisfaction with Singaporean hotels using online Airbnb reviews from 2011 to 2024. Sentiment analysis was performed on this digital word-of-mouth data. This study proposed a BERT-BiLSTM-Attention model to train and test on the public TripAdvisor dataset, employing Python libraries like SpaCy and Matplotlib for aspect extraction and data visualization. The analysis identified key aspects (hotel, room, staff, service, food) and revealed a predominantly positive outlook (95% positive reviews). This research contributes to the literature on using text mining and offers practical insights for enhancing service quality in the hotel sector by understanding tourist preferences.