Abstract
This paper describes the development of an emoticon recommendation system based on users’ emotional statements. In order to develop this system, an innovative emoticon database consisting of a table of emoticons with points expressed from each of 10 distinctive emotions was created. An evaluation experiment showed that our proposed system achieved an improvement of 28.1 points over a baseline system, which recommends emoticons based on users’ past emoticon selection. We also integrated the proposed and baseline systems, leading to a performance improvement of approximately 73.0 % in the same experiment. Evaluation of respondents’ perceptions of the three systems utilizing an SD scale and factor analysis is also described in this paper.
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Notes
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http://www.businessinsider.com/one-half-of-twitters-active-users-tweet-monthly-2013-11, retrieved on Nov. 25, 2013.
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http://www.kaomoji.sakura.ne.jp/, retrieved on Nov. 25, 2013.
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Urabe, Y., Rzepka, R., Araki, K. (2015). Comparison of Emoticon Recommendation Methods to Improve Computer-Mediated Communication. In: Ulusoy, Ö., Tansel, A., Arkun, E. (eds) Recommendation and Search in Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-14379-8_2
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DOI: https://doi.org/10.1007/978-3-319-14379-8_2
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