With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to extract and cluster the keywords of reviews texts. Finally, 10 first-level influencing factors of hotel customer satisfaction are determined: epidemic prevention, consumption emotion, convenience, environment, facilities, catering, target group, perceived value, price, and service. Based on backpropagation neural network and weight matrix operation, an influencing factor analysis model of hotel customer satisfaction is constructed to explore the role of these factors. The results show that consumption emotion, perceived value, epidemic prevention, target group, and convenience would significantly affect customer satisfaction, among which epidemic prevention becomes a new factor affecting customer satisfaction. Environment, facilities, catering, and service have relatively little effect on customer satisfaction, while price has the least effect. This study provides a path and method for online reviews of hotel management to improve customer satisfaction and provides a theoretical basis for the study of online reviews of hotels.

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We empirically examined the impact on consumer engagement of the matching of images and text, a format that is commonly used in product information advertising, by analyzing 322 advertisements posted by Estée Lauder on Sina Weibo between January 2020 and January 2021. The results indicated that when the external product information conveyed in the images matched the internal product attributes described in the text, this created cognitive coherence for consumers and promoted their engagement. Our findings provide guidance for brand managers to skillfully combine images and text in advertising to increase the engaging effect of social media advertisements on consumers.

Brands are increasingly using social media to create and manage posts to initiate and maintain consumer engagement. Based on the theory of consumer engagement, a perspective of brand post content, form and posting time is introduced to construct a conceptual model of consumer engagement for Sina Weibo. Rough set method and reduction algorithm of Holte 1 R are used to automatically generate the optimal decision rules. Rough set method does not need any prior knowledge and assumptions, which could effectively overcome the disadvantages of traditional statistical methods. The results show that entertainment content is easy to trigger moderate level of consumer engagement, the effect of information content on shares is significantly stronger than that of comments and likes, and the promotion content has an impact on liking. As the most vivid and the most interactive characteristic respectively, videos and questions significantly affect the mid-level consumer engagement. Keeping post length in the range of 16–50 characters stimulates the medium degree of sharing. Posts created on weekend promote the medium level of sharing and the low level of liking, while published at the peak or low peak period trigger the same level of sharing, but do not affect comments or likes. The study detects the characteristics that affect consumer engagement and define the scope of its role. The relationship and intensity of different characteristics on different levels of consumer engagement are effectively evaluated and identified by refining the association rules of consumer engagement, which are available for providing reference for brand managers to formulate social media marketing strategies.