Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue
Table 4: Predicting Low and High. Evaluation metrics: macro averaged Precision (P), Recall (R) and F1. * if p <0.05, ** if p <0.01. Improved performance compared to random is shown in boldface.
['[EMPTY]', 'P', 'Naive Bayes R', 'F1', 'P', 'Random Guessing R', 'F1']
We noticed the evaluation of Low and High showed promising performance depending on the traits. For example, for High in Extraversion, Precision (0.24), Recall (0.47) and F1 (0.31) were all statistically significantly higher than Precision (0.14), Recall (0.24) and F1 (0.17) for the baseline. After removing all Moderate samples, we trained and tested Naive Bayes classifiers. The absolute performance of the classifiers and the baseline were both improved compared to the prediction of Low, Moderate and High. This finding implies that it is easier for classifiers to distinguish between just Low and High. With respect to the prediction outcome after utilizing overlap dynamics features, we observed a performance improvement in predicting Low and High of Extraversion compared to the baseline. The precision, recall and F1 are all improved, and the improvement of F1 is statistically significant. In distinguishing between Low and High of Agreeableness, the classifier outperformed the baseline significantly. Note that there were some improvements in predicting Low and High of Conscientiousness and Open but they were not statistically significant.