An Empirical Study of Learning Based Happiness Prediction Approaches

In today’s society, happiness has attracted more and more attentions from researchers. It is interesting to study happiness from the perspective of data mining. In psychology domain, the application of data mining gradually becomes widespread and popular, which works from a novel data-driven viewpoint. Current researches in machine learning, especially in deep learningprovidenewresearchmethodsfortraditionalpsychologyresearchandbringnewideas.Thispaperpresentsanempiricalstudyoflearningbasedhappinesspredicitionapproachesandtheirpredictionquality.Conductedonthedataprovidedbythe“ChinaComprehensiveSocialSurvey(CGSS)”project,wereporttheexperimentalresultsofhappinesspredictionandexploretheinfluencingfactorsofhappiness.Accordingtothefourstagesoffactoranalysis,featureengineering,modelestablishmentandevaluation,thispaperanalyzesthefactorsaffectinghappinessandstudiestheeffectofdifferentensemblesforhappinessprediction.Throughexperimentalresults,itisfoundthatsocialattitudes(fairness),familyvariables(familycapital),andindividualvariables(mentalhealth,socioeconomicstatus,andsocialrank)havegreaterimpactsonhappinessthanothers.Moreover,amongthehappinesspredictionmodelsestablishedbythesefivefeatures,boostingshowsthemosteffectiveinmodelfusion.