Knowledge Representation for Emotion Intelligence

Abstract

Emotion intelligence (EI) is a traditional topic for psychology, sociology, biology and medical science. Because emotion is related with the personality, interpersonal effect, social function, disease treatment, etc. Analyzing the emotion from the Web data by computer technology becomes more and more popular, and the scientists from the non-computer domains need more helpful computing models to deal with professional problems that are not traditional for computer science. Knowledge representation is a basic and possible solution as a bridge between emotion intelligence and artificial intelligence. For the sentiment words, word embedding can map the words to vectors that represent the semantic context of the words. Sentiment embedding based on the word embedding can capture both semantics and the emotion information. We have introduced two kinds of improving embedding methods (MEC and Emo2Vec) for the sentiment words embedding. For emotion structure based on the psychology of emotion, knowledge graph can represent the cognitive relations between different emotion types. The same emotional expressions can affect the reaction and behaviors of the recipient in different ways due to factors such as social relations, information processing, time pressure, etc. Knowledge graph can represent these complicated situations as the relations between the entities and attributes. Based on this graph, we make the inference or prediction of the emotion influence on decision making.