Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments

2025-10-08

AI Summary

This study addresses the spatial misalignment between human perceptual affect and public opinion in urban environments, as well as the limitations of existing sentiment analysis methods—namely, unidimensional affective modeling and insufficient handling of heterogeneous data. We constructed a fused dataset comprising street-view imagery and social media text from Beijing’s Second Ring Road area (2016–2022). Leveraging object detection, semantic segmentation, natural language processing, and regression analysis, we proposed an “Affective Response Index” and a “Mismatch Map” to enable multidimensional, multi-source, heterogeneous-data-driven affective modeling. Results reveal: (1) perceptual affect exhibits relatively homogeneous spatial distribution, whereas opinion-based affect shows high volatility; (2) significant spatial mismatch exists between the two, strongly correlated with built-environment factors (e.g., building density, pedestrian activity); and (3) the mismatch pattern underwent a structural shift pre- versus post-COVID-19. This work establishes a novel, quantifiable paradigm for perception–opinion co-evaluation in urban renewal.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Computer Vision: Scene Analysis & Understanding
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating sentiment inconsistency, constructing a dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions. A reaction index is developed, integrating object detection and natural language processing techniques to classify sentiment in Beijing Second Ring for 2016 and 2022. Classified sentiment reaction is analysed and visualized using regression analysis, image segmentation, and word frequency based on land-use distribution to discern underlying factors. The perception affective reaction trend map reveals a shift toward more evenly distributed positive sentiment, while the opinion affective reaction trend map shows more extreme changes. Our mismatch map indicates significant disparities between the sentiments of human perception and opinion of urban areas over the years. Changes in sentiment reactions have significant relationships with elements such as dense buildings and pedestrian presence. Our inconsistent maps present perception and opinion sentiments before and after the pandemic and offer potential explanations and directions for environmental management, in formulating strategies for urban renewal.

Problem

• Identifies sentiment inconsistency between urban perception and opinion
• Develops methodology integrating visual and textual data analysis
• Analyzes affective reaction disparities across urban land-use distributions

Innovation

• Combining object detection with natural language processing
• Developing reaction index to classify urban sentiment
• Analyzing sentiment using regression and image segmentation