Automated Classification of Text Sentiment
Table 8. Test accuracy (%) on the summary and review data showing a comparison between GASA and CA-GASA. Ten-fold cross-validation was used, and all of the data sets had a size of 2000. Both GASA and CA-GASA were seeded with the HuLiu-6786 sentiment dictionary.
['[BOLD] Data set', '[BOLD] Summary Data  [BOLD] GASA', '[BOLD] Summary Data  [BOLD] CA-GASA', '[BOLD] Review Data  [BOLD] GASA', '[BOLD] Review Data  [BOLD] CA-GASA']
One drawback of CA-GASA is that the search space is significantly larger than GASA and as a result the training time is much longer. Given this drawback, CA-GASA was not tested against the other sentiment analysis algorithms.