Extracting human emotions at different places based on facial expressions and spatial clustering analysis

Yuhao Kang, Qingyuan Jia, Song Gao, Xiaohuan Zeng, Yueyao Wang, Stephan Angsuesser, Yu Liu, Xinyue Ye, Teng Fei

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

The emergence of big data enables us to evaluate the various human emotions at places from a statistical perspective by applying affective computing. In this study a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user-generated footprints collected from social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state-of-the-art computer vision techniques. Two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected from greater than 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research offers insights into integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS.

Original languageEnglish (US)
Pages (from-to)450-480
Number of pages31
JournalTransactions in GIS
Volume23
Issue number3
DOIs
StatePublished - Jun 2019

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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