Diener’s satisfaction with life scale, SWLS, is broadly used as a measure for estimating global life satisfaction in the literature. Despite the popularity of social media applications and numerous researches linking daily word usage to social sciences, none of the existing studies managed to identify solid negative and/or positive relations or any sign of irrelevance between the Facebook status updates and SWLS results of individuals. The main objective of this chapter is to fill this gap in the literature by investigating the relations between smileys and linguistic features in Facebook status updates and SWLS of individuals and choosing the most appropriate linguistic features among many others. The data analysis procedure presented includes correlation analysis, linear regression and support vector machine (SVM) regression methods. The results indicate that there are significant positive and negative correlations between SWLS and some word groups and the relations differ for male and female users. The procedure exposes that SVM regression is more suitable than the linear regression in identifying the relations between the word groups and SWLS. The results also reveal that the selected word groups can be further used to predict personal attributes using Facebook data. The results of the proposed procedure are discussed and compared with the research findings of the previous studies.