Multilingual Semantic Networks for Kansei Study
Words are communication media to share a concept in a community. A word involving ambiguity represents multiple concepts depending on contexts. Such ambiguity causes misunderstanding between people having different contexts. On the other hands, a community uses words to obtain responses from target population, such as customers and participants. The word ambiguity causes misunderstanding between a community and a target population due to different contexts. A community dealing with multiple languages (e.g. multinationals) has a difficulty in translation if there are no words in a second language, all meanings of which do not correspond to all meanings of a word one wishes to translate. To deal with word ambiguity, I proposed a multilingual semantic networks (MLSN) framework. The MLSN is a graph where multiple language words, as nodes, are semantically linked through concepts. I implemented WordNet of English, Japanese, and French into a graph database as a MLSN. I applied MLSN to following two analysis. In the first analysis, I investigated the meanings of ambiguous words such as “kansei”, and their semantic relations with relevant words in the three languages. I found that there are no words corresponding to all meanings of those words in second languages. In the second analysis, I discussed how MLSN supports to select and translate a set of words used as evaluation descriptors. I analyzed ten positive emotion words from Geneva Emotion Wheel and their translation. I demonstrated how MLSN automatically finds translation mismatches and semantic independence between emotion descriptors.
KeywordsMultilingual semantic network ambiguity translation difficulty
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- 1.Yanagisawa, H., Nakano, S., Murakami, T.: A Proposal of Kansei Database Framework and Kansei Modelling Methodology for the Delight Design Platform. Journal of Integrated Design and Process Science 1-12 (2016)Google Scholar
- 2.Yanagisawa, H.: Kansei quality in product design. Emotional engineering, pp. 289-310. Springer (2011)Google Scholar
- 3.De Saussure, F.: Course in general linguistics. Columbia University Press (2011)Google Scholar
- 4.Yanagisawa, H., Murakami, T., Noguchi, S., Ohtomi, K., Hosaka, R.: Quantification Method of Diverse Kansei Quality for Emotional Design: Application of Product Sound Design. In: ASME 2007 Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME, (2007)Google Scholar
- 5.Levy, P.: Beyond kansei engineering: The emancipation of kansei design. International Journal of Design 7, (2013)Google Scholar
- 6.Miller, G.A.: WordNet: a lexical database for English. Communications of the ACM 38, 39-41 (1995)Google Scholar
- 7.Favart, C., Esquivel Elizondo, D., Gentner, A., Mahut, T.: The Kansei Design Approach at Toyota Motor Europe. In: Fukuda, S. (ed.) Emotional Engineering Volume 4, pp. 119-144. Springer International Publishing, Cham (2016)Google Scholar
- 8.Harada, A.: Definition of Kansei, Evaluation of Kansei 2. Report of Modeling the evaluation structure of KANSEI (1998)Google Scholar
- 9.Scherer, K.R.: What are emotions? And how can they be measured? Social science information 44, 695-729 (2005)Google Scholar
- 10.Fontaine, J.J., Scherer, K.R., Soriano, C.: Components of emotional meaning: A sourcebook. OUP Oxford (2013)Google Scholar
- 11.Van de Vijver, F.J.R., Leung, K.: Methods and data analysis for cross-cultural research. Sage (1997)Google Scholar