# Multimodal Sentiment Classifier Framework for Different Scene Contexts
## 5 Tests, Results, and Discussion
### Image Sentiment Classification
It is important to remember that humans did the labeling of SIS & ISP, unlike Flickr. For the Flickr sub-data sets, the individual ISC models achieved accuracies of 84.54%, 83.21%, 68.53%, and 84.79% for the classes ONMM, OMM, IND, and IOwPB, respectively. For \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Number Units/Estimators** & \begin{tabular}{l} **Backbone** \\ **(Layers Trained)** \\ \end{tabular} & **Hyperparameters** & **Activation Function** \\ \hline Model DL\#1 & \begin{tabular}{l} \(n=1024\) \\ \(m=512\) \\ \end{tabular} & EfficientNetB0 (none) & \begin{tabular}{l} Opt: [PERSON] (\(1\times 10^{-4}\)) \\ Epochs: 20 \\ Batch size: 32 \\ \end{tabular} & \begin{tabular}{l} \(Xi=\text{ReLU}\) \\ \(i=\{1,\ldots,5\}\) \\ \end{tabular} \\ \hline Model DL\#2 & \begin{tabular}{l} \(n=1024\) \\ \(m=512\) \\ \end{tabular} & Xception (8) & \begin{tabular}{l} Opt: [PERSON] (\(1\times 10^{-4}\)) \\ Epochs: 20 \\ Batch size: 32 \\ \end{tabular} & \begin{tabular}{l} \(Xi=\text{ReLU}\) \\ \(i=\{1,\ldots,5\}\) \\ \end{tabular} \\ \hline Model DL\#3 & \begin{tabular}{l} \(n=512\) \\ \end{tabular} & ResNet50 (12) & \begin{tabular}{l} Opt: [PERSON] (\(1\times 10^{-4}\)) \\ Epochs: 20 \\ Batch size: 32 \\ \end{tabular} & \
\begin{tabular}{l} \(Xi=\text{ReLU}\) \\ \(i=\{1,\ldots,4\}\) \\ \end{tabular} \\ \hline RFa &
\begin{tabular}{l} \(k=100\) (est.) \\ \end{tabular} & - & - & - \\ \hline NNa & decided by the optimizer & - & Batch size: 32 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ISC_class, HSC, and ISC individual and ensemble models backbones and hyperparameters. SIS & ISP, the accuracies were 61.53%, 56.92%, 51.62%, and 49.06% for the same classes, respectively. The best result for each ISC class, HASC and ISC is marked in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline _Class_ & _Model_ & _Flickr Sub-Data Sets Accuracy_ & _SIS \& ISP Accuracy_ \\ \hline \multirow{4}{*}{ISC\_ONMM} & Model DL\#1 & 80.30\% & 53.82\% \\ \cline{2-4}  & Model DL\#2 & 77.87\% & 53.61\% \\ \cline{2-4}  & Model DL\#3 & 71.09\% & 50.23\% \\ \cline{2-4}  & RFa & **84.54\%** & 61.16\% \\ \cline{2-4}  & NNa & 83.34\% & **61.53\%** \\ \hline \multirow{4}{*}{ISC\_OMM} & Model DL\#1 & 79.31\% & 50.36\% \\ \cline{2-4}  & Model DL\#2 & 76.29\% & 53.40\% \\ \cline{2-4}  & Model DL\#3 & 55.26\% & 47.29\% \\ \cline{2-4}  & RFa & **83.21\%** & 55.93\% \\ \cline{2-4}  & NNa & 83.09\% & **56.92\%** \\ \hline \multirow{4}{*}{ISC\_IND} & Model DL\#1 & 64.53\% & 46.31\% \\ \cline{2-4}  & Model DL\#2 & 61.37\% & 49.65\% \\ \cline{2-4}  & Model DL\#3 & 55.47\% & 42.93\% \\ \cline{2-4}  & RFa & **68.53\%** & 50.21\% \\ \cline{2-4}  & NNa & 67.47\% & **51.62\%** \\ \hline \multirow{4}{*}{ISC\_IOwPB} & Model DL\#1 & 81.46\% & 44.85\% \\ \cline{2-4}  & Model DL\#2 & 77.65\% & 46.99\% \\ \cline{2-4}  & Model DL\#3 & 64.75\% & 46.41\% \\ \cline{2-4}  & RFa & **84.79\%** & **49.06\%** \\ \cline{2-4}  & NNa & 84.52\% & 48.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ISC_class, ISC, and HASC individual and ensemble models accuracy. Figure 7: ISC_OMM models’ confusion matrices: on the **left**, the model uses three inputs (which are the direct sentiments of the individual models), while the model on the **right** uses nine inputs (corresponding to the probabilities of the predicted sentiment). Another strong indicator is that the test accuracies are very close for the three individual models per class, and this consistency suggests that they are all effectively capturing the underlying patterns in the data, leading to a somehow similar performance. Additionally, the ensemble models play an important role because they enhance the accuracy of the best individual models by \(\sim\)4% to \(\sim\)5%, consistently in all classes. The same occurs for the global models. In addition, the ensembles offer stability in their responses as they are based on the multiple analyses of the other (individual) models.