We propose to predict the personalized emotion perceptions of images for each viewer. Different factors that may influence emotion perceptions, including visual content, social context, temporal evolution, and location influence are jointly investigated via the presented rolling multi-task hypergraph learning. For evaluation, we set up a large scale image emotion dataset from Flickr, named Image-Emotion-Social-Net, with over 1 million images and about 8,000 users. Experiments conducted on this dataset demonstrate the superiority of the proposed method, as compared to state-of-the-art.
The dynamic between speaker intent and listener perception is played out in the variation of acoustical cues by the speaker that must be interpreted by the listener to determine in an appropriate way. Emotion speech research must rely on either acted intent (i.e., an actor attempting to express an emotion) or listener perception (i.e., listening tests to assign emotional categories to non-acted data) to define ground truth labels for analysis. The emotion labels are described either using emotion dimension or emotion category. This study examines the two emotion characterization strategies dimension and category in communication of emotion embedded in speech as expressed through acted intent and the perception of emotion determined by a group of listeners. The results reveal that, without context information, intended emotion categories could be perceived by listeners with the averaged accuracy rate five times of chance in category. Also, the trend of listener ratings between emotion dimensions (valence/arousal) and emotional word categories was shown to be well correlated. Furthermore, while listeners confused the specific identity of certain emotional expressions, they were generally very accurate at identifying the intended affective space of the actor as determined by intended valence and arousal.
ABSTRACT Mirror Ritual is an interactive installation that challenges the existing paradigms in our understanding of human emotion and machine perception. In contrast to prescriptive interfaces, the work's real-time affective interface engages the audience in the iterative conceptualisation of their emotional state through the use of affectively-charged machine generated poetry. The audience are encouraged to make sense of the mirror's poetry by framing it with respect to their recent life experiences, effectively'putting into words' their felt emotion. This process of affect labelling and contextualisation works to not only regulate emotion, but helps to construct the rich personal narratives that constitute human identity. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.
Emotion affects our understanding of the opinions and sentiments of others. Research has demonstrated that humans are able to recognize emotions in various domains, including speech and music, and that there are potential shared features that shape the emotion in both domains. In this paper, we investigate acoustic and visual features that are relevant to emotion perception in the domains of singing and speaking. We train regression models using two paradigms: (1) within-domain, in which models are trained and tested on the same domain and (2) cross-domain, in which models are trained on one domain and tested on the other domain. This strategy allows us to analyze the similarities and differences underlying the relationship between audio-visual feature expression and emotion perception and how this relationship is affected by domain of expression.
Language is as important as expressions when reading emotion, a study has found -- meaning that being told someone looks'grumpy' can makes them seem grumpier. Researchers from Australia and the US asked volunteers to rate the emotions of people in either photographs or videos. The team found that when the participants were told that the subjects were feeling a specific emotion, this biased how they interpreted the expressions on show. The effect was most pronounced when dealing with angry, sad or scared faces -- as opposed to happy, disgusted, embarrassed, proud or surprised -- the team found. Language is as important as expressions when reading emotion, a study has found -- meaning that being told someone looks'grumpy' can makes them seem grumpier (stock image) 'The current studies demonstrate that language context alters the dimensional affective foundations that underlie our judgements of others' expressions,' the researchers wrote in their paper.