Research on emotional space for movie and TV drama videos

Emotional space refers to a multi-dimensional emotional model that describes a group of subjective feelings or emotions. Since the existing discrete emotional space is mainly aimed at human’s primary emotions, it cannot describe the complex emotions evoked when watching movies. In order to solve this problem, an emotional fusion space for videos was constructed by selecting movies and TV dramas with rich emotional semantics as the research objects. Firstly, emotional words based on movie and TV drama videos are acquired and analyzed by using subjective evaluation and semantic analysis methods. Then, the emotional word vectors obtained from the above analysis are fused, reduced dimension by t-distributed stochastic neighbor embedding(t-SNE) algorithm, and clustered by bisecting K-means clustering algorithm to get a discrete emotional space for movie and TV drama videos. This emotional fusion space can obtain different categories by changing the value of the emotion classification number without re-labeling and calculation.

Emotional inference by means of Choquet integral and λ-fuzzy measurement in consideration of ambiguity of human mentality

Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.

Emotion and Cognitive Reappraisal Based on GSRWearable Sensor

Various wearable equipment enables us to measure people behavior by physiological signals.In our research,we present one galvanic skin reaction(GSR)wearable sensor which can analyze human emotions based on cognition reappraisal.First,We research the factors of emotional state transition in Arousal-Valence-Stance(AVS)emotional space.Second,the influence of the cognition on emotional state transition is considered,and the reappraisal factor based on Gross regulation theory is established to correct the effectiveness from cognitive reappraisal ability to emotional state transition.Third,based on the previous work,we establish a GSR emotion sensing system for predicting emotional state transition and considering the correlation between GSR signals and emotions.Finally,an overall wearable sensor layout is built.In the experiment part,we invited 30 college students to wear our GSR sensors and watch 14 kinds of affective videos.We recorded their GSR signals while asking them to record their emotional states synchronously.The experiment results show different reappraisal factors can predict subjects’emotional state transition well and indirectly confirm the feasibility of the Gross regulation theory.

Dimensional emotion recognition in whispered speech signal based on cognitive performance evaluation

The cognitive performance-based dimensional emotion recognition in whispered speech is studied.First,the whispered speech emotion databases and data collection methods are compared, and the character of emotion expression in whispered speech is studied,especially the basic types of emotions.Secondly,the emotion features for whispered speech is analyzed,and by reviewing the latest references,the related valence features and the arousal features are provided. The effectiveness of valence and arousal features in whispered speech emotion classification is studied.Finally,the Gaussian mixture model is studied and applied to whispered speech emotion recognition. The cognitive performance is also considered in emotion recognition so that the recognition errors of whispered speech emotion can be corrected.Based on the cognitive scores,the emotion recognition results can be improved.The results show that the formant features are not significantly related to arousal dimension,while the short-term energy features are related to the emotion changes in arousal dimension.Using the cognitive scores,the recognition results can be improved.

The Space Between my Grandparents Family Photos Take Grandchild on Emotional Journey into the Past

IN our family album, there is a picture of my grandfather, Fang Xiaoyue, and grandmother, Ma Junwan. The picture was taken in the 1920s when my grandparents were a newly married couple. It seemed to be in the afternoon in the photo, and in the background was an elegant garden, pale clouds in the sky, a rockery close by and a pavilion

Personalized Emotion Space for Video Affective Content Representation

A personalized emotion space is proposed to bridge the ＂affective gap＂ in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean （FCM） clustering algorithm is adopted to divide the emotion space. Gaussian mixture model （GMM） is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A （Velance-Arousal） emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.