Discovering Basic Emotion Sets viaSemantic Clustering on a Twitter Corpus
Table 6.2: DELSAR clustering accuracy using a dimension of 36. Each set uses a subcorpus comprised of 500, 360, 280 and 100 documents of each emotion in the set, 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] DELSAR']
In addition to performing DELSAR1000, we tested four subcorpora of varying document sizes to observe any temporal effects. We took document sizes of 500, 360, 280 and 100 and distributed equally across the whole corpus using a modulus function on the index. Ekman’s set proved to be the best overall, in line with DELSAR1000, although it was outperformed by Oatley’s set when lower amounts of documents were used. Again, in all cases, Russell’s categories were outperformed by all other sets, and our DELSAR set outperformed each optimal set by as much as 8.8%.