  A plethora of words are used to describe the spectrum of human emotions, but
how many emotions are there really, and how do they interact? Over the past few
decades, several theories of emotion have been proposed, each based around the
existence of 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 present research based on a theory that people transmit
their understanding of emotions through the language they use surrounding
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), evaluating the distinctiveness of the semantic
meaning attached to the emotional label. We hypothesise that the more distinct
the 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 in fact the most semantically distinct
overall. 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). We also demonstrate how using LSC data can help visualise
emotions. We introduce the concept of an Emotion Profile and briefly analyse
compound emotions both visually and mathematically.
