Predicting Human Trustfulness from Facebook Language
Table 3: Comparing the performance of our language model with sentiment as baseline, using different feature sets: ngr_r: ngrams as relative frequencie, ngr_b: ngrams as boolean variables. Bold indicates the best performance. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error.
['[BOLD] Features', '[BOLD] Pearson  [ITALIC] rdis', '[BOLD] MSE']
Our best model (ngr_r+ngr_b+topics) had an 8% reduction in mean squared error over sentiment, and achieved a Pearson correlation coefficient of rdis=.494 which is considered a large relationship between a behavior (language use) and a psychological trait Meyer et al. and just below state-of-the-art language-based assessments of other personality traits Park et al.