Evolutionary Influence Maximization in Viral Marketing

Abstract

This paper describes the development of an emoticon recommendation systembasedonusers’emotionalstatements.Inordertodevelopthissystem,aninnovative emoticon database consisting of a table of emoticons with points expressed fromeachof10distinctiveemotionswascreated.Anevaluationexperimentshowed 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 alsointegratedtheproposedandbaselinesystems,leadingtoaperformanceimprovement 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.