## 5 Conclusions  
In this study, we performed systematic experiments to classify emojis into six classes of emotions based on the [PERSON] model. We ran the experiments using traditional supervised classifiers trained on deep features extracted using AlexNet and ResNet-18 pre-trained networks, and five pre-trained deep CNNs trained using transfer learning. The traditional  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{3}{*}{**Classifier**} & \multirow{3}{*}{**Optimizer**} & \multicolumn{4}{c}{**Learning Rate**} \\  & & **0.01** & **0.001** & **0.0001** & **0.00001** \\  & & & **Achieved Accuracy** & & \\ \hline \multirow{3}{*}{**AlexNet**} & adam & 16.67 & 16.67 & 94.55 & **97.86** \\  & sgdm & 16.67 & 16.67 & 96.69 & 95.51 \\  & rmsprop & 16.67 & 16.67 & 92.95 & 97.65 \\ \hline \multirow{3}{*}{**GoogleNet**} & adam & 16.67 & 0.8472 & 98.29 & 96.69 \\  & sgdm & 16.67 & 0.9786 & 97.33 & 82.26 \\  & rmsprop & 16.67 & 16.67 & **98.40** & 97.54 \\ \hline \multirow{3}{*}{**InceptionV3**} & adam & 82.37 & 95.51 & **99.47** & 93.91 \\  & sgdm & 98.4 & 98.61 & 93.91 & 75.53 \\  & rmsprop & 79.81 & 96.37 & 98.18 & 94.76 \\ \hline \multirow{3}{*}{**MobileNetV2**} & adam & 54.8 & 97.43 & 97 & 94.76 \\  & sgdm & 97.54 & **98.61** & 95.72 & 70.72 \\  & rmsprop & 35.04 & 95.61 & 98.29 & 96.68 \\ \hline \multirow{3}{*}{**SqueezeNet**} & adam & 16.67 & 30.66 & 90.81 & 83.76 \\  & sgdm & 16.67 & 90.17 & 89.2 & 65.38 \\ \cline{1-1}  & rmsprop & 16.67 & 16.67 & **96.04** & 86.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of deep classifier by varying the learning rate and optimizer. The bold numbers are to highlight the best results.  
classifiers K-NN and SVM achieved 94.97% accuracy using ResNet-18 and AlexNet features, respectively, while the decision tree and LPBoost achieved the lowest accuracies of 65.49% and 63.14%, respectively, using AlexNet features. The highest accuracy of 99.47% was achieved by the InceptionV3 model, while MobilNetV2 and GoogleNet performed better compared to the other two deep classifiers AlexNet, SqueezeNet, and traditional supervised classifiers.  
Future works can operate multimodal approaches, e.g., merging our work with natural language processing techniques for deeper context analysis. Recurrent neural networks can be adopted for GIF-based emojis and memes over different platforms. However, there is currrently a scarcity of available datasets labeled with emotions. A critical point should thus be the creation of a sound dataset including emotions expressed by memes providing a sufficient variability of data to correctly classify the emotions of related pictograms.  
Idea and conceptualization, V.F.; methodology, V.F., M.A.; software, M.A.; validation, V.F., M.A.; formal analysis, V.F., M.A.; investigation, V.F, M.A.; resources, V.F., M.A.; data curation, V.F., M.A.; writing--original draft preparation, V.F., M.A.; writing--review and editing, V.F.; visualization, V.F., M.A.; supervision, project administration, and funding acquisition, V.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EmoRe.  
Not applicable.  
Data Availability Statement: Data are not availablle to the public.  
The authors thank [PERSON] from University of Perugia for their kind support and contribution to this project.  
The authors declare no conflict of interest. This work added more than 50% of new content to the previous, and includes no copy-and-paste from any previous version. The content, contextualization, and discussion are changed. The images are changed. Only experimental results overlap the conference paper of which this paper is an extension.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SMS & Short Message Service \\ GIF & Graphic Interchange Format \\ K-NN & k-nearest neighbors \\ SVM & Support Vector Machine \\ LDA & Linear Discriminant Analysis \\ CNN & Convolutional Neural Networks \\ RBF & Radial Basis Functions \\ ReLU & Rectified Linear Units \\ \end{tabular}