In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users' diversified emotions on specific hot event. A modified topic-supervised biterm topic model is utilized in CECM to detect emotion topics' in event-related tweets, and then context-sensitive topical PageRank is utilized to detect meaningful multiword expressions as emotion causes. Experimental results on a dataset from Sina Weibo, one of the largest microblogging websites in China, show CECM can better detect emotion causes than baseline methods.
To produce emotional artificial systems in AI domain, usually a subset of human emotional states are imported to the target domain and the major differences between natural and artificial domains are often ignored. In this paper we will discuss about why such an approach is not useful for all possible applications of emotions and we will show how it is necessary and possible to produce artificial emotion systems based on the target systems goals, abilities and needs.
Humanity is in a constant race towards making computers and other gadgets smarter than humans and enabling them to carry out elaborate processes and activities that are only deemed possible by us mere mortals as of now. One such power is perceiving human emotions. Till date, only humans have been granted this ability to be able to detect and gauge the emotions of the people surrounding us and then act accordingly in a set environment. However now, computers could have been empowered enough to do the same and in quite an efficient manner! The researchers of MIT Media Lab have been successful in creating models of machine learning that can "read" facial expressions to understand the emotions of humans.