The hotel industry has been facing fierce competition in recent years. It is important for hotels to conduct effective strategic planning for competitiveness improvement to achieve sustainable development. Prior studies on hotel strategic planning mainly use questionnaire data or hotel internal data, which have the problems of insufficient data or neglecting customer perspectives. The purpose of this study was to develop an integrated method for customer-oriented strategic planning for hotel competitiveness improvement based on text mining of online reviews. First, text mining of online reviews was conducted to extract customer-concerned service attributes and evaluate customer concern level and the performance of the service attributes through Latent Dirichlet Allocation (LDA) and sentiment analysis. Second, the competitive structures of the hotels were analyzed and the main competitors were identified from the competitive hotels through correspondence analysis. Third, SWOT analysis of the target hotel toward the main competitors was conducted, and the priorities of factors in each SWOT category were determined. An empirical study on a five-star hotel is given to illustrate the feasibility and effectiveness of the proposed method. The results indicate that the proposed method can help managers in strategic planning to obtain more specific strategies for hotel competitiveness improvement.

The number of online textual reviews on each hotel aspect can reflect the tourist preference difference on distinct aspects. Therefore, not only online textual reviews but their numbers have a significant impact on tourists’ hotel selection decisions. Motivated by this observation, this study proposes a hotel ranking model for hotel selection based on the sentiment analysis of online textual reviews by considering the differences in the number of reviews on different aspects. We explicitly model the differences in the number of reviews on aspects through the confidence interval estimation. In addition, the AS-Capsules model, which can jointly perform aspect detection and aspect-level sentiment classification with high accuracy, is employed for sentiment analysis. We conducted a case study on TripAdvisor.com, the experimental results show that our proposed model is able to effectively assist the tourists in making the desirable decision on hotel selection.