Employing Valence-Arousal On Representation And Reasoning Of Product's Implicit Emotion

The objective of traditional KANSEI engineering is to create a questionnaire to obtain users' perceptual evaluation of products by using Paired Kansei Adjectives (PKA). However, this approach is only to use the emotional vocabulary of things under a coarse-grained description in the emotional semantic characterization, the use of the scale is discrete and linear; perceptual knowledge modeling in this way exits significant imprecision and uncertainty.This thesis proposed a new cognitive model of emotional semantics on three aspects:the basic data acquisition, representation and reasoning model of product implicit emotion.First, the emotional thesaurus (Affective Norms for English Words, ANEW) experimental methods was adopted to conduct psychological experiments by artificial calibration and to establish the connections between body's visual perception (Valence-Arousal, which valence denotes the degree of excited or calm, arousal denotes the degree of positive or negative) under the "natural state" and emotional words. Second, the emotional Valence-Arousal based emotional cellular model was established by using Valence-Arousal data which obtained from the manual calibration psychological experiments, including emotional cellular-based kernel and shell definition and its learning methods of measurement functions; at the same time, the boundaries of the ambiguity of a cellular (Fuzziness) was calculated. Third, the representation model of implicit emotion was founded based on Valence-Arousal emotional cellular; a fuzzy similarity analysis of emotional cellular based on fuzzy set was introduced and the design methodology of an implicit emotional retrieval system of product was proposed. Finally, reasoning model of products implicit emotion based on Valence-Arousal was presented respectively by using CBR (Case Based Reasoning), Bayesian synthesis based variable precision Rough Sets method and MFSVM (Multi-classifier Fuzzy Support Vector Machine) techniques are given to demonstrate the effectiveness of the proposed methodology in cases study of mobile phone and vehicle design.The main contributions of this thesis include:(1) Established the Valence-Arousal based emotion cellular representation model of product implicit emotion which is the intention and in-depth analyzing of these "discrete" KANSEI adjectives that is to find a balance point under the qualitative and quantitative analysis, so that each word is no longer isolated emotional, blunt, but a "cell."(2) Established the kernel and shell's definition and its acquisition method of Valence-Arousal based emotion cellular, which included the mapping relations between emotional cellular and the two-dimensional Valence-Arousal emotional space and the definitions of three type kernels (single point type, flat type and circle type). The Gaussian singular and mixture models were applied to describe the density of points on Valence-Arousal space for difference types and the parameters of Gaussian singular and mixture model are acquired by parameters estimation methods.(3) Proposed the computing methods of fuzzy boundary of emotional cellular model, which included establishing boundaries and the calculation model by fuzzy set and fuzzy logic operations such as intersection, union; and the calculation process of boundary for the kernels on single-point type, flat type and circle type respectively. It plays an important role for implicit emotion computing to acquire the exact classification of emotions by using boundaries computing of emotional cellular.(4) Established similarity measure model between Valence-Arousal based emotional cellulars which included the definition of similarity metric space, the formal definition of similarity relation and similarity calculations; introduced a similarity computing model on IF-THEN rules of knowledge representation and also demonstrated the design methodology of the product's implicit emotional retrieval system by using the proposed similarity computing model.(5) Established three kinds of reasoning models on product's implicit emotion retrieving based on Valence-Arousal. In the fuzzy case based reasoning model, a product typical feature set was regarded as the initial case, the corresponding density function of emotion cellular was applied to calculate the similarity degree and obtain the overall evaluation of product by using the fuzzy nearest neighbor algorithm (Near Neighbor, NN). In the Bayesian learning model, rule based representation of production emotion cellular and its measurement (LN, LS) employing variable precision rough set theory was used to this synthesis model. As in multi-classifier model, the multi-classifier fuzzy support vector machine technology combined density distribution function of emotional cellular was applied to build the classification model of product's implicit emotion.