The Conceptualisation of Emotion Qualia: Semantic Clustering of Emotional Tweets

A plethora of words are used to describe the spectrum of human emotions, but how many distinct emotions exist, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each founded upon a set of basic emotions, and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we propose a theory that people transmit their understanding of emotions through the language they use that surrounds mentioned emotion keywords. Using a labelled corpus of over 21,000 tweets, six of the basic emotion sets proposed in existing literature were analysed using Latent Semantic Clustering (LSC) to propose the distinctiveness of the semantic meaning attached to the emotional label. We hypothesise that the more distinct language is used to express a certain emotion, then the more distinct the perception (including proprioception) of that emotion is, and thus more basic. This allows us to select the dimensions best representing the entire spectrum of emotion. We find that Ekman’s set, arguably the most frequently used for classifying emotions, is the most semantically distinct. Next, taking all analysed (that is, previously proposed) emotion terms into account, we determine the optimal semantically irreducible basic emotion set using an iterative LSC algorithm. Our newly-derived set (Accepting, Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a 6.1% increase in distinctiveness over Ekman’s set (Angry, Disgusted, Joyful, Sad, Scared).