Mining hotel social sensing data and analyzing its spatial and temporal characteristics can provide decision support for hotel managers. Present research on this topic is limited to the overall hotel industry and text mining. Here, we first obtain POI and reviews for star-rated hotels in Nanchang from 2018 to 2021. Secondly, the hotel POI (Point of Interest) is combined with the sentiment value of customer reviews. Finally, comparative analysis and topic mining of Spatio-temporal aspects of text reviews of different star-rated hotels are conducted using sentiment analysis, spatial analysis, and thematic social network analysis. Results show that: (1) Hotel star rating and hotel review sentiment value are significantly positively correlated. The seasonal trends of different star rating hotel sentiment values are similar, but are highest in summer and lower in autumn; (2) The highest sentiment value is seen for friends’ outings and the lowest is for business trips; (3) Customer reviews of star-rated hotels focus on three aspects: facilities, service, and location. Three-star hotels focus on the stay experience, while four-star hotels focus on the breakfast situation. Exploring hotel social sensing data can intuitively illustrate hotel selection’s behavioral patterns and spatial-temporal characteristics. The methods of this study can expand the application of social sensing data in different fields, such as the tourism and restaurant industries.

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor.