Understanding human emotions is a complex problem. Various tools and techniques have been employed by researchers across various domains to understand and infer human emotions. With the advent of smart machines, the problem took a different angle to try and machines themselves make to understand the emotions of its user (s). Human emotions had to be labeled and then were organized with inverse emotions being placed opposite to each other. Various continuous as well as discrete emotion-mapping models were proposed to identify and quantify human emotions. This paper is the result of studying around fifteen such models and comparing them with each other to come to a more concrete viewpoint towards understand human emotions. © 2017 ACM.