Abstract

To better serve the public, urban attractions must understand the public's likes and dislikes and continually adapt and optimize their service in response. However, traditional methods for gauging public opinion, such as questionnaires and individual interviews, cannot meet the need for large-scale, time-efficient, and high-precision public preference surveys: only large volumes of online tourist review data will suffice. This study takes Beijing and Shenzhen, the two top-tier cities in North and South China, as subjects to conduct an online review of visitor data for urban attractions in the two cities. Specifically, the influence of attraction features on public perception is quantitatively analyzed from three dimensions—spatial, temporal and resource type—which are combined with Natural Language Processing (NLP), Textual Data Mining, and Econometric analysis. The results show that the influence of four types of features—First impression, Economical, Service and Environmental features—on public perception remains largely consistent under different model conditions. However, different landscape types and attributes affect preferences in different ways. To be precise, water has radically different effects on public preferences in the north and south, with water features in urban attractions in the south contributing less to positive public perceptions. Meanwhile, water features significantly increase preferences for natural landscapes. Furthermore, public perceptions were found to be heavily influenced by public health emergencies (COVID-19). In the two cities studied, the proportion of positive reviews was higher after 2020 than before, as were the regression model indices. Drawing on these results, several suggestions are put forward for the optimization and adaptation of urban attractions.

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