Weibo sentiment analysis validation dataset

Humans spend most of their time in settlements, and the built environment of settlements may affect the residents’ sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size. This study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments. The results show the following: 1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, more security facilities, and neighborhoods close to water are more likely to improve the residents’ sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. 2) The number of security facilities, the proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value and the number of security facilities are basic needs and exhibit nonlinear correlations with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments. The quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents.

人类绝大多数时间都居于聚居区之中，而聚居区的建成环境可能会对居民的情绪体验产生影响。该领域的研究属于交叉学科范畴，融合了城市规划与公共卫生两大方向。然而，该领域的研究长期受制于主观情绪量化难度较高、样本量偏小两大瓶颈。本研究采集了2017年厦门市共计147613条微博（Weibo）文本签到数据，以此量化该市1096个街区的居民情绪水平。本研究采用多层回归模型与梯度提升决策树（Gradient Boosting Decision Tree, GBDT），探究街区与街道两级建成环境对居民情绪的多层级与非线性影响效应。研究结果如下：1）多层回归模型结果显示，在街区尺度上，较高的土地价值、较低的容积率（plot ratio）、较低的人口密度、更多的安防设施，以及临近水体的街区，更有助于提升居民的情绪体验；在街道尺度上，更多的绿地与商业用地占比、更少的工业用地、更高的建筑密度与道路密度，以及较少的流动人口规模，更易催生居民的积极情绪。居民情绪的总变异中，约有19%来源于街道间的差异。2）安防设施数量、绿地与商业用地占比，以及建筑密度与道路密度，均与居民情绪呈线性相关关系；土地价值与安防设施数量属于基础需求要素，与居民情绪呈非线性相关关系；容积率、人口密度、工业用地占比与流动人口占比则属于进阶需求要素，与居民情绪呈非线性相关关系。通过对情绪的量化分析，本研究可确定建成环境对街区及周边区域居民情绪的影响阈值。本研究结果可为城市规划工作提供数据支撑，助力制定针对性措施以优化居民的居住环境。