# Roman Urdu Sentiment Analysis Using Transfer Learning
## 5 Results and Discussions
The comparison of the two phases of the transfer learning process, along with the accuracy, is shown in Table 8.  
Figure 4: 4C-CNNA evaluation on RUSA-19 and RUSA dataset: (**a**) classification accuracy of RUSA-19 on training and validation samples; (**b**) cross-entropy loss on RUSA-19 for training and validation samples (**c**) classification accuracy of RUSAD on training and validation samples; (**d**) cross-entropy loss on RUSAD for training and validation samples. As it is clear from the results, the accuracy of the proposed model is lower when it is used directly on the target domain without training. This only signifies that source domain knowledge is not entirely enough to achieve high accuracy on the target domain. In the second phase, the performance and accuracy of the proposed model increased significantly with the use of incremental training on the target domain. The sizable range of the source domains, along with fine word embeddings, also provided support for learning more contextual information. All of the factors combined resulted in the proposed model remarkably improving the accuracy of the classification process. The proposed model reflects different classification accuracy of source domains, and this indicates that in the transfer learning process, choosing a source domain corresponding \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline RF [82] & 0.780 & 0.780 & 0.780 & 0.773 \\ \hline LR [83] & 0.770 & 0.772 & 0.770 & 0.769 \\ \hline SVM [58] & 0.851 & 0.860 & 0.855 & 0.854 \\ \hline NB [84] & 0.821 & 0.822 & 0.821 & 0.815 \\ \hline KNN [59] & 0.809 & 0.802 & 0.800 & 0.797 \\ \hline LSTM [64] & 0.873 & 0.862 & 0.867 & 0.864 \\ \hline RCNN [63] & 0.831 & 0.838 & 0.824 & 0.820 \\ \hline CNN [65] & 0.825 & 0.823 & 0.823 & 0.818 \\ \hline
4C-CNNAT & 0.911 & 0.914 & 0.917 & 0.908 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 4C-CNNAT performance comparison with various ML/DL classifiers using RUR corpus. Figure 5: (**a**) classification accuracy on source datasets with different activation functions; (**b**) classification accuracy on source datasets with different Dropout values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline RF [82] & 0.712 & 0.725 & 0.690 & 0.707 \\ \hline LR [83] & 0.706 & 0.700 & 0.695 & 0.607 \\ \hline SVM [58] & 0.740 & 0.750 & 0.714 & 0.731 \\ \hline NB [84] & 0.716 & 0.731 & 0.690 & 0.709 \\ \hline KNN [59] & 0.710 & 0.714 & 0.670 & 0.791 \\ \hline LSTM [64] & 0.783 & 0.782 & 0.797 & 0.774 \\ \hline RCNN [63] & 0.751 & 0.733 & 0.754 & 0.720 \\ \hline CNN [65] & 0.759 & 0.756 & 0.753 & 0.743 \\ \hline
4C-CNNAT & 0.881 & 0.884 & 0.887 & 0.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 4C-CNNAT performance comparison with various ML/DL classifiers using RUDS corpus. to a target domain is an essential task. Some datasets have highly unmatched classification variables, which result in lower accuracy. However, the size and vocabulary metrics of either source or target dataset can also affect the performance. We also concatenated the source datasets to train the model and evaluated the target datasets. Table 9 presents the accuracy obtained on the target dataset using transfer learning from the concatenated source dataset. As it is obvious that the proposed model achieved higher accuracy on the target datasets when trained on the merged datasets of the source domain. The reason for this higher accuracy could be that merging different datasets increases the domain similarity of the source and target domain. We have compared the proposed model's performance on the target datasets. 4C-CNNAT achieved higher accuracy on target datasets as compared to the state-of-the-art models. We trained the proposed model on the source datasets and transferred learning on each dataset and compared the accuracy.