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.
This paper proposes an SFNN (a sales factor model using a neural network), which uses a backpropagation multilayer perceptron neural network and weight matrix operation, to study the mechanism of the influencing factors of online product sales in the e-commerce platform. To achieve this objective, this study analyzes the factors and relative strength of online product sales based on four aspects: online reviews, review system curation, online promotional marketing, and seller guarantees. The empirical analysis of the SFNN model based on the data of Taobao.com shows whether the 14 factors, in relation to the four aspects, have any impact on product sales. In addition, the findings indicate that the number of sentiment words greatly affects product sales. Other factors affecting online product sales significantly include the review volume, the number of uploaded pictures, the negative review rate, the discount rate, 7+ day returns and money-back guarantees, and the freight insurance. This study examines the interactions among the various factors affecting product sales on the e-commerce platform and provides management inspiration for ecommerce enterprises to manipulate online reviews, undertake effective promotion and fulfill after-sales promises.
The stability of the real-estate market is crucial to China’s economic development and, in times of crisis, the economy will experience systemic adverse reactions that require appropriate regulation by the state using tax policy tools. Therefore, we analyzed the impact of real-property tax on house prices using panel data for 31 provinces in China from 2009 to 2020 using an empirical method, i.e., the instrumental variables approach. The empirical results show that each of the previous property-related taxes actually contributed to the increase in house prices and did not have a dampening effect. The newly introduced property tax will lead to a decline in house prices, which will help to alleviate the overheating of real-estate investment and mitigate the real-estate bubble crisis. A rational view of the impact of a property tax on housing prices needs to be taken in the context of factors such as income levels, consumer price levels, loan rates, and Chinese consumer culture. In order to achieve the goal of “no speculation in housing”, we also need to pay attention to the regulating effect of a property tax in combination with many other factors. This study is important for promoting property tax reform, curbing overheated real-estate investment, and promoting healthy economic development.
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