Project

Human Emotion In Daily Life

Goal: Exploring the interaction of human emotion and urban structure.

Updates
0 new
0
Recommendations
0 new
0
Followers
0 new
2
Reads
0 new
192

Project log

Yingjing Huang
added 3 research items
Various fields have widely used place emotion extracted from social networking sites (SNS) information in recent years. However, the emotional information may contain biases as users are a particular subset of the whole population. This research studies whether there are significant differences between place emotion extracted from SNS and the place in-situ (a campus of Wuhan University). Two datasets from different sources, Weibo (a platform similar to twitter) and in-situ cameras, are collected over the same time periods in the same geographical range. By utilizing online cognitive services on the photos collected, the diversity of people with a recognizable face in terms of age, gender, and emotions are determined. The results suggest that there are significant differences in place emotion extracted from Weibo and in-situ. Furthermore, the pattern of differences varies among diverse demographic groups. This paper quantitatively contrasts place emotion extracted from SNS and the place in-situ, which can help researchers achieve a more profound understanding of human behavior differences between online and offline place emotion. This research also provides a theoretical basis to calibrate the emotion metrics obtained from SNS facial expressions on future place emotion studies.
Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content.
Various fields have widely used place emotion extracted from social networking sites (SNS) information in recent years. However, the emotional information may contain biases as users are a particular subset of the whole population. This research studies whether there are significant differences between place emotion extracted from SNS and the place in-situ (a campus of Wuhan University). Two datasets from different sources, Weibo (a platform similar to twitter) and in-situ cameras, are collected over the same time periods in the same geographical range. By utilizing online cognitive services on the photos collected, the diversity of people with a recognizable face in terms of age, gender, and emotions are determined. The results suggest that there are significant differences in place emotion extracted from Weibo and in-situ. Furthermore, the pattern of differences varies among diverse demographic groups. This paper quantitatively contrasts place emotion extracted from SNS and the place in-situ, which can help researchers achieve a more profound understanding of human behavior differences between online and offline place emotion. This research also provides a theoretical basis to calibrate the emotion metrics obtained from SNS facial expressions on future place emotion studies.
Teng Fei
added a research item
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.
Yingjing Huang
added a project goal
Exploring the interaction of human emotion and urban structure.