This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.

Emotion recognition from text using semantic labels and separable mixture models

https://scholarworks.utep.edu/cgi/viewcontent.cgi?article=1498&context=cs_techrep

Fuzzy logic and its applications in medicine.

Image emotional semantic research is a promising and challenging issue. This paper analyzes the emotional space and builds a novel scheme that can annotate the image emotion semantic automatically and realize emotional image retrieval. Based on psychological experiments evaluating evoked feelings for art paintings, we first identify an orthogonal three-dimension emotional factor space of images through 12 pairs of emotional words. Then, the following three novel image features are designed for each emotional factor to predict it. They are luminance-warm-cool fuzzy histogram, saturation-warm-cool fuzzy histogram integrated with color contrast and luminance contrast integrated with edge sharpness. The values of emotional factors can be predicted from the image features automatically by using support vector machine of regression. Finally, we design and implement an emotion-based image retrieval system, which enables the users to perform retrieval using emotional semantic words. Experimental results show the effectiveness of our model.

Image Retrieval by Emotional Semantics: A Study of Emotional Space and Feature Extraction

Emotional semantic image retrieval is a new and promising research direction in recent years. This paper attempts to introduce this emerging area to researchers, give them a brief overview of current research progress and framework. In this field three key issues, emotional semantic representation, image feature extraction, and emotion recognition, are discussed in detail with approaches and challenges. In addition, future research directions are suggested.

A survey on emotional semantic image retrieval

With the growing importance of information technology in our everyday life, new types of applications are appearing that require the understanding of information in a broad sense. Information that includes affective and subjective content plays a major role not only in an individual's cognitive processes but also in an individual's interaction with others. We identify three key points to be considered when developing systems that capture affective information: embodiment (experiencing physical reality), dynamics (mapping experience and emotional state with its label) and adaptive interaction (conveying emotive response, responding to a recognized emotional state). We present two computational systems that implement those principles: MOUE (Model Of User Emotions) is an emotion recognition system that recognizes the user's emotion from his/her facial expressions, and from it, adaptively builds semantic definitions of emotion concepts using the user's feedback; MIKE (Multimedia Interactive Environment for Kansei communication) is an interactive adaptive system that, along with the user, co-evolves a language for communicating over subjective impressions.

Modeling Multimodal Expression of User's Affective Subjective Experience

This paper introduces the image database system which can retrieve using impression words. ART MUSEUM (Multimedia Database with Sense of Color and Construction upon the Matter of ART) is our prototype system for advanced image database. It allows users to retrieve images from a database of impressionist paintings by using adjective words. The system learns the correlation between the feature of sample paintings and user's descriptions, and provides interactive and progressive learning mechanisms. We show the interesting experimental results on ART MUSEUM system.

Image retrieval system using impression words

On the stability of Lagrange programming neural networks for satisfiability problems of prepositional calculus

The two theories concerning mixed emotions, adopted in this research is briefly explained. Also discussed are the image codes linked to the mixed emotional words and the way to realize a mechanism using fuzzy inference, in which the internal emotion of a simulated person reacts on a sequence of inputted terms and evokes some emotional change. The relevant part of the theory for subjective observation model to the application of emotion processing system is briefly explained. The construction methodology of the system, which has two functions, is described; one function is that the evoked emotion is transitive depending on the observation, and the other is that the aggregated emotion by the several subjective observations settles to the objective emotion. Some attractive results obtained from the simulated emotion processing system are shown: what kinds of emotions are evoked, and how the emotional transition is done by subjective observations, when a poem is used for the input signal. Lastly, the more expanded emotional Dialog System is introduced.

An emotion processing system based on fuzzy inference and subjective observations

These days, the development of computer devices leads to computer user explosion On the other hand, conventional technology cannot handle user diversity, because the computer cannot treat each user's subjectivity It is important to materialize techniques which can answer the user's subjective requests flexibly for the coming new media epoch The article focuses on studying how subjective impressions are grounded in physical perceptions, especially in visual perception We propose a model for subjectivity and physical perceptions, and give an example in an image database system

Torao Yanaru

Papers

Image retrieval system using impression words

On the stability of Lagrange programming neural networks for satisfiability problems of prepositional calculus

An emotion processing system based on fuzzy inference and subjective observations

A study of database system with KANSEI information

An emotion processing system based on fuzzy inference and subjective observations