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The system is comprised of an affect analysis system and an original emoticon database: a table of 59 emoticons numerically categorized by 10 emotion types. The authors' experiments showed that 73.0% of chosen emoticons were among the top five recommended by the system, which is an improvement of 43.5% over the method used in current smartphones, which is based only on users' past emoticon selections.<\/p>","DOI":"10.4018\/ijmdem.2014010102","type":"journal-article","created":{"date-parts":[[2014,6,11]],"date-time":"2014-06-11T08:27:47Z","timestamp":1402475267000},"page":"14-33","source":"Crossref","is-referenced-by-count":4,"title":["Emoticon Recommendation System to Richen Your Online Communication"],"prefix":"10.4018","volume":"5","author":[{"given":"Yuki","family":"Urabe","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafal","family":"Rzepka","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Araki","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijmdem.2014010102-0","unstructured":"Ashcraft. 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