DISCO PAL: Diachronic Spanish Sonnet Corpus with Psychological and Affective Labels
Table 15: Partial dependence analysis between GAM features and their annotated counterparts (II)
['[BOLD] label Dramatización', '[BOLD] feature anger', '[BOLD] r2 0.88', '[BOLD] p-val 0.67', '[BOLD] coeff 0.02', '[BOLD] sig no', '[BOLD] label Idealización', '[BOLD] feature anger', '[BOLD] r2 0.92', '[BOLD] p-val 0.01', '[BOLD] coeff 0.11', '[BOLD] sig yes']
As mentioned before, a linear regression model is trained over all sonnets, using all GAM features as independent variables, and using one of the annotated features as dependent variable. Then, we get the p-value of the corresponding GAM feature, and see if that value is relevant, using a threshold of 0.1. (p<0.1 meaning it is significant). We also check the coefficient of that feature to see that it is >0 (a negative coefficient would mean that even if the model is fitted properly, the relationship between both features is not coherent). We also check the adjusted r-squared value in order to see if the model is well-fitted.