Abstract:As the most common environment for urban residents to contact, the quality of the street will bring different degrees of emotional experiences to the surrounding residents. Therefore, it’s essential to explore the relationship between the urban street environment and emotional experience. With a case study conducted in Beijing Road Historical and Cultural Blocks, Guangzhou, this paper aims to study the relationship between multiple street characters and sentiments and predict street sentiments. Multiple street characters were extracted through Open Street Map, Baidu Street View, and Gaode POI datasets. The Tencent Cloud Semantic Analysis Tool is used to obtain the positive sentiment probability of check-in data at each location, which was partially used as the sentiment value of streets. Correlation analysis was used to study the relationships between multiple street characters and sentiments. The artificial neural network regress model was trained to predict the sentiments in all streets in the study area. The results show that the visibility of buildings has a positive relationship with sentiment values, while the visibility of vegetation and cars has a negative relationship with sentiment values. Unit-length street sentiments are the highest in the core area of Beijing Road cultural blocks, which decline as the streets depart from the central axis. More negative sentiments appear in the southwest area, far from the central axis, where restoration and improvement are needed. It is recommended that the quality of the street be upgraded through the repair of building facades along the street, the introduction of high-end businesses, and the optimization of pedestrian and vehicular flow planning.
