A semantic network approach to affect analysis: A case study on depression

Abstract

This paper discusses the semantic network approach to identify affects in natural language input and focusses on representing spatio-temporal affect information. It has been observed that this approach performs better in analysis of affect information that can be effectively utilized for the prognosis of human cognitive behavior. The research work in this paper describes a new approach towards simple representation of multi-dimensional affect data that facilitates the provision of emotions or affects with varying granularity. The analysis algorithm thus executed on the semantic representation generates temporally significant behavior patterns. The analysis of the semantic network generates temporally significant behavior patterns. The framework has been designed to be extensible over a wide variety of applications in cognitive computing.