Extracting human emotions at different places based on facial expressions and spatial clustering analysis
This work addresses the integration of human emotions into geographic and GIS studies to enhance understanding of sense of place, though it is incremental as it applies existing methods to new data.
The study tackled the problem of measuring human emotions at different places by analyzing facial expressions from georeferenced photos, resulting in a happiness ranking of 80 tourist attractions based on over 2 million faces from 6 million photos, with emotional variation linked to factors like openness.
The emergence of big data enables us to evaluate the various human emotions at places from a statistic 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 in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using the state-of-the-art computer vision techniques. And 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 out from over 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 may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS.