Discovering Basic Emotion Sets viaSemantic Clustering on a Twitter Corpus
Table 7.1: DELSAR1100 Clustering Matrix.
['[EMPTY]', '[BOLD] Emotion interested', '[BOLD] Emotion joyful', '[BOLD] Emotion miserable', '[BOLD] Emotion pleased', '[BOLD] Emotion relaxed', '[BOLD] Emotion sad', '[BOLD] Emotion scared', '[BOLD] Emotion sleepy', '[BOLD] Emotion stressed', '[BOLD] Emotion surprised']
Firstly, we visualise what a two and three-dimensional emotion space looks like. We can see that this matrix illustrates the notion that each emotion is in fact a combination of all other emotions. This property has also been shown in \possessiveciterussell and more recently \possessiveciterobots work. Generally speaking, the primary benefit of a graph is to quickly identify correlations and anomalies. Plotting emotion profiles provides us with a graphic visualisation of emotion, so we can easily identify key differences in the composition of emotions. Where the emotion element is equal to the emotion being profiled, the average of adjacent cells is plotted to avoid skewing. In this section we briefly introduce the concept of emotion equations. While Plutchik theorises on which combinations of emotions create another emotion, we attempt to provide the first significant empirical evidence that emotions can in fact be combined to create other emotions, to some extent.