Discovering Basic Emotion Sets viaSemantic Clustering on a Twitter Corpus
Table 6.1: DELSAR clustering accuracy of each basic emotion set using a corpus comprised of 1000 documents for each emotion within each set. Standard Deviation of all models is σ=0.027.
['[EMPTY]', '[BOLD] Model  [BOLD] Izard', '[BOLD] Model  [BOLD] Russell', '[BOLD] Model  [BOLD] Plutchik', '[BOLD] Model  [BOLD] Ekman', '[BOLD] Model  [BOLD] Tomkins', '[BOLD] Model  [BOLD] Oatley', '[BOLD] Model  [BOLD] All', '[BOLD] Model  [BOLD] DELSAR']
We performed DELSAR1000 Recall that DELSAR creates an LSA space of all documents within each emotion set; for each basic emotion set an LSA space of (1000×number_of_emotions) documents is created. Evaluating all sets, our results show the accuracy of clustering each document to its nearest document, whether it is the same or another emotion. Out of the theories analysed, Ekman’s set proved to be the most semantically distinct, with a 2.9% increase in accuracy compared to the average of the remaining sets. Russell’s categories performed worst, which is surprising seeing as these emotions were taken as the basis for representing the entire emotion spectrum as a whole.