EVALUATING EMOTIONAL PERCEPTION OF SPATIAL HOTSPOTS VIA DEEP LEARNING: A CASE STUDY OF SHANGHAI

CUI Lu-ming; QU Ling-yan; HE Dan

doi:10.13959/j.issn.1003-2398.2021.05.015

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HUMAN GEOGRAPHY ›› 2021, Vol. 36 ›› Issue (5) : 121-130,176. DOI: 10.13959/j.issn.1003-2398.2021.05.015

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EVALUATING EMOTIONAL PERCEPTION OF SPATIAL HOTSPOTS VIA DEEP LEARNING: A CASE STUDY OF SHANGHAI

CUI Lu-ming^1,2, QU Ling-yan², HE Dan^1,2

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Abstract

The study on spatial emotional perception which arises from the intersection between Spatial Psychology and Geography is an important field of Human Geography. But its development has long been limited by data access and methods. Deep learning can provide the strong support for the quantitative analysis of spatial emotional perception. This paper attempts to build a deep learning framework which based on social media sign-in data to evaluate and analyze spatial emotional perception. In this work, emotional perceptions were classified into six categories, such as joy, affection, distress, angry, disgust and null. The pre-trained language model named Bidirectional Encoder Representations from Transformers was used to analyze the emotional expression of active users and generate the emotional perception map. Compared with the traditional sentiment analysis model, the Bidirectional Encoder Representations from Transformers has a greater degree of improvement in correctness. Then part-of-speech analysis was applied to the comments extracted to investigate the cause of the emotional perception. In the last, this paper used Lexical Analysis of Chinese, a lexical analysis tool developed by Baidu, for lexical analysis and named entity recognition. In total, 813,633 geotagged social media data and 1619 POI were collected from Shanghai. The main findings were as follows:1) The most popular sign-in locations in Shanghai are concentrated in the inner-city, transportation hubs and important public facilities. 2) The proportion of positive emotions shows an overall decrease with the increase of the distance to the city center. 3) Producers' emotional perception of various activity spaces were mostly positive opinions, so the results of commentary viewpoint extraction were similar.

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deep learning / BERT model / sign-in map / emotion map / perception evaluation

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CUI Lu-ming, QU Ling-yan, HE Dan. EVALUATING EMOTIONAL PERCEPTION OF SPATIAL HOTSPOTS VIA DEEP LEARNING: A CASE STUDY OF SHANGHAI[J]. HUMAN GEOGRAPHY. 2021, 36(5): 121-130,176 https://doi.org/10.13959/j.issn.1003-2398.2021.05.015

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