Automatic Extraction of Personality from Text:Challenges and Opportunities
TABLE VI: Cross validated accuracy for all the binary models on D{HR} and D{LR}
['[BOLD] Factor', '[ITALIC] D{HR} ACC{LM}', '[ITALIC] D{HR} ACC{SVR}', '[ITALIC] D{HR} ACC{Dum}', '[ITALIC] D{LR} ACC{LM}', '[ITALIC] D{LR} ACC{SVR}', '[ITALIC] D{LR} ACC{Dum}']
We consider our [−3,3] interval-based representation to be preferable, as it is sufficiently granular to express realistic nuances while remaining simple enough not to overtax annotators with too many choices. Nevertheless, to gain some understanding of how our approach would compare to the state of the art, we modified our methods to train binary classifiers on the large and small datasets. For the purposes of this training a factor value below zero was regarded as low and values above as high, and the classifiers learnt to distinguish only these two classes. Note that a direct comparison with earlier systems is problematic due to the differences in datasets. This test merely serves to ensure that our approach is not out of line with the general performance in the field.