Abstract
Kansei engineering methods and text mining methods were applied to the web application for assisting sightseeing travel at Kure city. Kansei engineering methods used here were a questionnaire and multivariate analyses for research inter-relations between aims of travels and interests. Text mining was done on logged tweets on Twitter. The number of tweets mentioned on Kure was around 3400 to 3700 tweets per day. The text mining reveals latest events and people’s interests in the daily basis. With Twitter text mining to conventional Kansei engineering methods, both general Kansei on sightseeing and rapidly changing interests are kept reflecting to the inference rules of the web-application.
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References
Nagamachi, M.: An image technology expert system and its application to design consultation. Int. J. Hum. Comput. Inter. 3(3), 267–279 (1991)
Nagamachi, M., Senuma, I., Iwashige, R.: A study of emotion technology. Jpn. J. Ergon. 10(2), 121–130 (1974). (in Japanese)
Nagamachi, M., Lokman, A.M.: Innovations of Kansei Engineering. CRC Press, Boca Raton (2011)
Nagamachi, M. (ed.): Kansei/Affective Engineering. CRC Press, Boca Raton, Florida (2011)
Nagamachi, M., Lokman, A.M.: Kansei Innovation: Practical Design Applications for Product and Service Development. CRC Press, Boca Raton (2015)
Rosen, A.: Tweeting Made Easier, Twitter official blog (2017). https://blog.twitter.com/official/en_us/topics/product/2017/tweetingmadeeasier.html
Higuchi, K.: A two-step approach to quantitative content analysis: KH coder tutorial using anne of green gables (part I). Ritsumeikan Soc. Sci. Rev. 52(3), 77–91 (2016)
Higuchi, K.: A two-step approach to quantitative content analysis: KH coder tutorial using anne of green gables (part II). Ritsumeikan Soc. Sci. Rev. 53(1), 137–147
Watanabe, H.: Reboot memories – memory inheritance based on communication emerged by flowing records. Ritsumeikan Heiwa Kenkyu (Ritsumeikan Peace Study) 19(1), 1–12 (2018)
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Ishihara, S., Nagamachi, M., Tsuchiya, T. (2019). Development of a Kansei Engineering Artificial Intelligence Sightseeing Application. In: Fukuda, S. (eds) Advances in Affective and Pleasurable Design. AHFE 2018. Advances in Intelligent Systems and Computing, vol 774. Springer, Cham. https://doi.org/10.1007/978-3-319-94944-4_34
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DOI: https://doi.org/10.1007/978-3-319-94944-4_34
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