Redirigiendo al acceso original de articulo en 18 segundos...
ARTÍCULO
TITULO

GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human?Environment Relationships

Yingjing Huang    
Teng Fei    
Mei-Po Kwan    
Yuhao Kang    
Jun Li    
Yizhuo Li    
Xiang Li and Meng Bian    

Resumen

In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human?environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible.

 Artículos similares

       
 
Minwoo Park and Euichul Lee    
In this paper, we propose a method for extracting emotional factors through audiovisual quantitative feature analysis from images of the surrounding environment. Nine features were extracted such as time complexity, spatial complexity (horizontal and ver... ver más
Revista: Future Internet

 
Konlakorn Wongpatikaseree, Sattaya Singkul, Narit Hnoohom and Sumeth Yuenyong    
Language resources are the main factor in speech-emotion-recognition (SER)-based deep learning models. Thai is a low-resource language that has a smaller data size than high-resource languages such as German. This paper describes the framework of using a... ver más

 
Sheetal Kusal, Shruti Patil, Ketan Kotecha, Rajanikanth Aluvalu and Vijayakumar Varadarajan    
Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people?s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts i... ver más

 
Antonella D?Amico and Domenico Guastella    
This paper discusses new ideas about the use of educational robotics in social-emotional learning. In particular, educational robotics could be a tool intended to allow children to acquire some of the basic aspects of human emotions and emotional functio... ver más
Revista: Future Internet