What a Dialogue!A Deep Neural Framework for Contextual Affect Detection
Table 3: Experimental results on Friends and EmotionPush datasets. F(E) denotes that the training is done on the corresponding dataset while F+E indicates that training is done on both Friends and EmotionPush dataset.
['[BOLD] Framework Used', '[BOLD] Train Data', '[BOLD] Friends Dataset  [ITALIC] WA', '[BOLD] Friends Dataset  [ITALIC] UWA', '[BOLD] EmotionPush Dataset  [ITALIC] WA', '[BOLD] EmotionPush Dataset  [ITALIC] UWA']
In the table, HiGRU and HiGRU-sf are proposed by [jiao2019higru]. Results show that our model outperforms all the other models with a significant margin. Further, we observe the improvement to be statistically significant with 95% and 99% confidence on Friends and EmotionPush dataset, i.e., the p-value is less than 0.05 for paired T-test[kim2015t] of both the datasets. On Friends dataset, our model reports un-weighted and weighted accuracy of 74.39% and 75.94% as compared to that of 68.9% and 74.4% of the state-of-art models, thus improving them by 5.49% and 1.54%. On the other hand, our CAD framework performs better on EmotionPush dataset as well, reporting the unweighted and weighted accuracies of 80.18% and 86.24%, respectively, as compared to that of 70.2% and 77.1% in the state-of-art model, thus giving an increment of 9.14% and 9.98%. It is worth noticing that the accuracy of anger in EmotionPush dataset increases by 17.38 points without compensating with the other classes. This implies that for all the emotion classes our framework performs reasonably competent with the accuracies being balanced and not biased to any specific class.