Detecting Egregious Conversations between Customers and Virtual Agents
Table 2: Cross-validation results for the baselines and EGR models.
['Model', 'Egregious P', 'Egregious R', 'Egregious F', 'Non-Egregious P', 'Non-Egregious R', 'Non-Egregious F']
Specifically, for the egregious class, the precision obtained by the text-based and EGR models were similar. This indicates that the text analyzed by both models encodes some information about egregiousness. On the other hand, for the recall and hence the F1-score, the EGR model relatively improved the text-based model by 41% and 18%, respectively. We will further analyze the models below.