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This study aims to provide a method for classifying travellers’ requirements to help hoteliers understand travellers’ requirements and improve hotel services. Specifically, this study develops a strength-frequency Kano (SF-Kano) model to classify the requirements expressed by travellers in online reviews.

The strength and frequency of travellers’ requirements are determined through sentiment and statistical analyses of the 13,217 crawled online reviews. The proposed method considering the interaction between strength and frequency is proposed to classify the different travellers’ requirements.

This study identifies 13 travellers’ requirements by mining online reviews. According to the results of the improved Kano model, the six travellers’ requirements belong to one-dimensional requirements; two travellers’ requirements belong to must-be requirements; three travellers’ requirements belong to attractive requirements; two travellers’ requirements belong to indifferent requirements.

Results of this research can guide hoteliers to address hotel service improvement strategies according to the types of travellers’ requirements. This study can also expand the analysis scope of hotel online reviews and provide a reference for hoteliers to understand travellers’ requirements.

By mining online reviews, this study proposes an SF-Kano model to classify travellers’ requirements by considering both the strength and frequency of requirements. This study uses the optimisation model to determine the classification thresholds. This process maximises travellers’ satisfaction at the lowest cost. The classification results of travellers’ requirements can help hoteliers gain a deeper understanding of travellers’ requirements and prioritise service improvements.