# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 3 Results and Discussion
### Method of Analysis and Classifiers
#### 3.1.1 Performance Analysis for Inferring Emotion Tags at Top-Level
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Classifier** & **Demographic** & **Specificity** & **Precision** & **Recall** & **F-Measure** & **Accuracy** \\ \hline \multirow{4}{*}{**F1-measure**} & \multirow{4}{*}{**F2-measure**} & Male & 0.506 & 0.851 & 0.850 & 0.850 & 85.01\% \\ \cline{3-8}  & & & Female & 0.507 & 0.849 & 0.849 & 0.849 & 84.86\% \\ \cline{3-8}  & & & Combine & 0.514 & 0.825 & 0.822 & 0.823 & 82.23\% \\ \cline{3-8}  & & & Male & 0.509 & 0.841 & 0.841 & 0.841 & 84.11\% \\ \cline{3-8}  & & & Female & 0.510 & 0.835 & 0.835 & 0.834 & 83.48\% \\ \cline{3-8}  & & & Combine & 0.513 & 0.823 & 0.827 & 0.824 & 82.67\% \\ \cline{3-8}  & & & Male & 0.667 & 0.793 & 0.769 & 0.758 & 76.93\% \\ \cline{3-8}  & & & Female & 0.530 & 0.787 & 0.766 & 0.753 & 76.55\% \\ \cline{3-8}  & & & Combine & 0.538 & 0.758 & 0.738 & 0.724 & 73.78\% \\ \hline \multirow{4}{*}{**F1-measure**} & \multirow{4}{*}{**F2-measure**} & Male & 0.790 & 0.523 & 0.789 & 0.790 & 79\% \\ \cline{3-8}  & & & Female & 0.782 & 0.525 & 0.782 & 0.782 & 78\% \\ \cline{3-8}  & & & Combine & 0.747 & 0.535 & 0.746 & 0.747 & 75\% \\ \cline{3-8}  & & & Male & 0.729 & 0.541 & 0.728 & 0.729 & 73\% \\ \cline{3-8}  & & & Female & 0.729 & 0.541 & 0.728 & 0.729 & 73\% \\ \cline{3-8}  & & & Combine & 0.749 & 0.535 & 0.749 & 0.749 & 75\% \\ \cline{3-8}  & & & Male & 0.674 & 0.559 & 0.721 & 0.674 & 65\% \\ \cline{3-8}  & & & Female & 0.662 & 0.563 & 0.667 & 0.662 & 66\% \\ \cline{3-8}  & & & Combine & 0.664 & 0.562 & 0.718 & 0.664 & 64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of inferring emotion tag performance for three different classifiers at top and bottom–level using K-NN. Moreover, Figure 6 provides the confusion matrix for the best case top-level inferring emotion tag analysis obtained by K-NN for male, female, and combined emotion tag responses. It can be observed from Figure 6, in the case of male-based emotion tag analysis, 2318 amusement emotion instances are truly classified out of 2868 instances. On the other hand, for emotion tag responses from the combined category, 1182 out of 1440 instances are truly classified for the amusement emotion. Similarly, 153 instances are truly classified from 187 instances when inferring sad emotion tags for male emotion responses. In the same way, for the female and combined emotion tags, 151 and 155 instances are truly classified out of 187 instances, for the sad emotion class, respectively. Inferring emotions using K-NN classifier indicates that it is easier to predict amusement, awe, boredom, excitement, disgust, fear, contentment, anger and sad emotion tags based on object categories. Moreover it is observed Figure 5 that inferring gender-based emotions using different object categories is more effective as compared to combined emotion inferring. Therefore, combined emotion inferring results are not as effective as those based on female and male emotion responses. However, the overall inferring emotion performance is practicable and sufficient. Figure 5: Best-case average F1-score for inferring emotion tags at top-level with nine different emotions using K-NN. Figure 6: Best-case confusion matrices obtained for inferring emotion tags based on top-level object categories using K-NN classifier with (**a**) male responses, (**b**) female responses, (**c**) combined responses. The columns represent the predicted values, while the rows represent the ground truths. Here, e1, e2, e3,..., e9 represent Amusement, Anger, Awe, Boredom, Contentment, Disgust, Excitement, Fear, and Sad emotion, respectively.