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A plethora of words are used to describe the spectrum of human emotions, buthow many emotions are there really, and how do they interact? Over the past fewdecades, several theories of emotion have been proposed, each based around theexistence of a set of 'basic emotions', and each supported by an extensivevariety of research including studies in facial expression, ethology, neurologyand physiology. Here we present research based on a theory that people transmittheir understanding of emotions through the language they use surroundingemotion keywords. Using a labelled corpus of over 21,000 tweets, six of thebasic emotion sets proposed in existing literature were analysed using LatentSemantic Clustering (LSC), evaluating the distinctiveness of the semanticmeaning attached to the emotional label. We hypothesise that the more distinctthe language is used to express a certain emotion, then the more distinct theperception (including proprioception) of that emotion is, and thus more'basic'. This allows us to select the dimensions best representing the entirespectrum of emotion. We find that Ekman's set, arguably the most frequentlyused for classifying emotions, is in fact the most semantically distinctoverall. Next, taking all analysed (that is, previously proposed) emotion termsinto account, we determine the optimal semantically irreducible basic emotionset using an iterative LSC algorithm. Our newly-derived set (Accepting,Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a6.1% increase in distinctiveness over Ekman's set (Angry, Disgusted, Joyful,Sad, Scared). We also demonstrate how using LSC data can help visualiseemotions. We introduce the concept of an Emotion Profile and briefly analysecompound emotions both visually and mathematically.

Discovering Basic Emotion Sets via Semantic Clustering on a Twitter Corpus