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In the hotel industry, the measurement and evaluation of customer satisfaction is the principal tool for marketers to assess the health of their relationships with customers. Online review (OR) is an important vehicle of the measured and evaluated results. In recent years, both academia and the industry have attached great importance to the measurement and evaluation of customer satisfaction based on online reviews. However, there are several problems with the satisfaction measurement and evaluation of hotel customers: the diverse dimensions of indices, the static method of measurement, and the contradiction between conclusions. Thus, it is urgent to standardize the theories, methods, and techniques for measurement and evaluation. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The proposed framework consists of five steps: (1) The corpus of online reviews is obtained, laying a data basis for subsequent research. (2) From the perspective of managers, the useful texts are recognized through review preprocessing, word segmentation, extraction of high-frequency words, topic extraction, phrase processing, and selection of subjective phrases. (3) Based on the recognized useful texts, a three-layer index system is created for hotel customer satisfaction according to relevant professional theories. (4) The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, calculate index weight based on word frequency statistics, and then dynamically measure the customer satisfaction. (5) The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction. The feasibility of the framework and its specific methods were demonstrated through a case study on the online reviews of two five-star hotels on Ctrip.com and comparison against the conclusions in relevant literature and manual tags.

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews