# Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data
## 4 Discussion and Conclusions  
The alleviation of negative emotions and the enhancement of positive emotions among residents have long been identified as key strategies for improving the quality of life in urban areas. The ongoing advancement of big data technology has facilitated analyses of the multifaceted factors influencing residents' emotional experiences, taking into account the distribution patterns of diverse environmental elements at the spatial scale. In the context of rapid urbanization, we focus on Lixia District of Jinan City as a case study. This study employs the comprehensive utilization of urban streetscape big data and deep learning methodologies. It focuses on the emotional perceptions of street residents as the core research object, utilizing urban streetscape big data to ascertain the location perceptions of expansive urban areas. Furthermore, it adopts urban color characteristics as the research perspective, elucidating the influence mechanisms of the complexity and coordination of colors in the streetscape on the emotional perceptions of residents. The principal findings of this study are as follows:  
1. The predominant color types in Lixia District of Jinan City are gray and blue-green. Gray is distributed in a more concentrated manner, while blue-green is distributed in a more scattered manner. Additionally, the majority of areas in Lixia District exhibit a high color complexity and low color coordination. 2. There is a high degree of correlation between emotional perceptions, with a high positive correlation between beauty, security, and richness and a moderate positive correlation between boredom and depression. The prevalence of depression and liveliness is higher in the central area, while the perception of safety and richness is lower in this region. As distance from the center increases, residents' depression levels decline, liveliness levels rise, and emotional distributions exhibit a regular aggregation phenomenon. Positive emotions are consistent in areas of a high spatial value, while negative emotions display the opposite spatial distribution characteristics. 3. The complexity and coordination of colors are pivotal elements influencing the emotional perceptions of residents. A greater degree of color complexity tends to provide a more diverse visual stimulus, which, in turn, attracts the attention of residents, particularly in commercial areas and entertainment venues. However, an excess of complexity may result in visual fatigue, which may, in turn, elicit feelings of depression and unease. In contrast, a moderate color complexity has been found to stimulate vitality and pleasure, thereby inducing feelings of relaxation and ease. Meanwhile, color coordination has been demonstrated to have a more pronounced positive effect on residents' emotional experience. Highly coordinated color combinations have been shown to contribute to a more harmonious and aesthetically pleasing streetscape, and may enhance residents' sense of security and happiness. In contrast, poorly coordinated colors have been found to increase visual confusion and lead to emotional discomfort and tension. This study makes a significant contribution to both the theoretical and practical understanding of residents' emotional perceptions. Theoretically, the study validates the argument that \"environmental color harmony can enhance the visitor experience\", which is in line with existing research results on residents' emotions. Furthermore, this study enriches the theoretical framework of the impact of color features (combinations) on cities. In terms of methodology, the objective of this study was to propose a quantitative methodology that enables the automated and efficient measurement of human perceptions of large-scale urban environments. This approach involves the combination of street-level imagery, representing specific locations, and deep learning methods to facilitate the comprehension of images at a high level of abstraction.