Public Perception of Built Environment in Urban Street: A Text Emotion Analysis Approach

Abstract

Liveability can be measured by various factors that matter for quality of life, but people’s perception and feelings toward the city, especially the built environment, is considered fundamental to the evaluation of urban liveability. Most previous studies described liveability-oriented urban built environments as spaciousness, bright and convenient with objective indicators, but attributes such as pleasant and comfortable are difficult to assess objectively. Traditional methods such as questionnaire surveys or interviews are likely to produce bias with small sample size or are time consuming if collecting large sample of data. Development of big data and machine learning approach makes it possible to evaluate people’s subjective perceptions toward the built environment. Exploring an urban street area in Shanghai, this research applies a Chinese natural language processing (NLP) tool to the text database and assesses the public perception toward built environment through a 0-1 score system. NLP is a machine learning technology that enables computers to interpret, manipulate and comprehend human language. The result indicates that the NLP emotion analysis is able to quantify people’s perceptions toward built environment and reveals the extent of the perceptions, which would significantly aid human-centred design of urban built environment.

Keywords: built environment, natural language processing, emotion analysis, perception, Shanghai

DOI: 10.54941/ahfe1005608