Discovering Basic Emotion Sets viaSemantic Clustering on a Twitter Corpus
Table 6.4: ELSA average cosine values using a dimension of 50. Each emotion uses a subcorpus comprised of 500, 360, 280 and 100 documents, selected using a modulus function on the index.
['[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] STDEV', '[BOLD] Model  [BOLD] ELSA']
A standard deviation of 0.1% suggests that there is negligible temporal effects on the corpus, with Tomkin’s set again outperforming all other sets, in line with our results from ELSA1000, and our ELSA set outperforming Tomkin’s set in each case.