Application of EmotionML

In the recent years, increasing attention has been paid to the area of affective computing, which deals with the complex phenomenon of human emotion. Therefore, a model for describing, structuring, and categorizing emotional states of users is required. The dimensional emotion theory is one of widely used theoretical foundations for categorization of emotions. According to the dimensional theory, emotional states are projected to the affective space, which has two dimensions: valence and arousal. In order to navigate in the affective space, Cartesian coordinate system is used, where emotion quality is defined by combination of valence and arousal. In this paper, we propose another representation of the affective space with polar coordinate system. The key advantages of such a representation include (1) capability to account not only for emotion quality, but also for emotion intensity, (2) reasonable explanation of the location of neutral emotion in the affective space, and (3) straightforward interpretation of the meaning of an emotional state (quality defined by angle and intensity defined by distance from the origin). Although in our experiment most of the induced emotions can be differentiated with polar coordinate system, further investigation is still needed to find out either Cartesian or polar coordinates system represents affective space better in practice.

There is growing interest about personalizing a computer's responses according to a user's affective states (emotions). This is particularly useful for learning, health care, and entertainment systems. However, systems able to recognize, understand, and respond to human affect are still designed and developed from scratch and the experience from their implementation is rarely documented, resulting in forcing developers to "re--invent the wheel" each time. This paper presents an approach to record the design knowledge related to affective systems in the form of patterns, aiming to develop a pattern language for them.

Arxiv preprint arXiv:0912.1810, 2009

The ability of an intelligent environment to connect and adapt to real internal sates, needs and behaviors' meaning of humans can be made possible by considering users' emotional states as contextual parameters. In this paper, we build on enactive psychology and investigate the incorporation of emotions in pervasive systems. We define emotions, and discuss the coding of emotional human markers by smart environments. In addition, we compare some existing works and identify how emotions can be detected and modeled by a pervasive system in order to enhance its service and response to users. Finally, we analyze closely one XML-based language for representing and annotating emotions known as EARL and raise two important issues which pertain to emotion representation and modeling in XML-based languages.

Data

Affective understanding is an area of affective computing which is concerned with advancing the ability of a computer to understand the affective state of its user. This area continues to receive attention in order to improve the human-computer interactions of automated systems and services. Systems within this area typically deal with big data from different sources, which require the attention of data engineers to collect, process, integrate and store. Although many studies are reported in this area, few look at the issues that should be considered when designing the data pipeline for a new system or study. By reviewing the literature of affective understanding systems one can deduct important issues to consider during this design process. This paper presents a design model that works as a guideline to assist data engineers when designing data pipelines for affective understanding systems, in order to avoid implementation faults that may increase cost and time. We illustrate the f...

Arxiv preprint arXiv:0912.2479, 2009

The ability of an intelligent environment to connect and adapt to real internal sates, needs and behaviors' meaning of humans can be made possible by considering users' emotional states as contextual parameters. In this paper, we build on enactive psychology and investigate the incorporation of emotions in pervasive systems. We define emotions, and discuss the coding of emotional human markers by smart environments. In addition, we compare some existing works and identify how emotions can be detected and modeled by a pervasive system in order to enhance its service and response to users. Finally, we analyze closely one XML-based language for representing and annotating emotions known as EARL and raise two important issues which pertain to emotion representation and modeling in XML-based languages.

International Journal of Engineering and Technology, 2011

The analysis of emotions can be performed on various sources of input, namely: text, speech or voice, images, and videos. This paper discusses a graph-based technique that prepares the ground for flexible and expandable emotion analysis from text documents. We take into account the complexities of human emotion representation in text that is more challenging and susceptible to inconsistent outcomes. The design of the proposed system aims towards a generalized solution for such kinds of text mining applications and emphasizes a simplified (yet flexible) emotion representation and analysis system that can be subjected to fine-tuned analysis, depending on future requirements. The aim of our work is to develop a prototype that can be used for (unsupervised) learning from some given text and can be used for possible extension of the emotion dictionary.

Emotionally annotated databases are repositories of multimedia documents with annotated affective content that elicit emotional responses in exposed human subjects. They are primarily used in research of human emotions, attention and development of stress-related mental disorders. This can be successfully exploited in larger processes like selection, evaluation and training of personnel for occupations involving high stress levels. Emotionally annotated databases are also used in multimodal affective user interfaces to facilitate richer and more intuitive human-computer interaction. Multimedia documents in emotionally annotated databases must have maximum personal ego relevance to be the most effective in all these applications. For this reason flexible construction of subject-specific of emotionally annotated databases is imperative. But current construction process is lengthy and labor intensive because it inherently includes an elaborate tagging experiment involving a team of hum...