# Roman Urdu Sentiment Analysis Using Transfer Learning
## 5 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline RF [82] & 0.770 & 0.770 & 0.770 & 0.763 \\ \hline LR [83] & 0.760 & 0.762 & 0.760 & 0.759 \\ \hline SVM [58] & 0.841 & 0.850 & 0.845 & 0.844 \\ \hline NB [84] & 0.811 & 0.812 & 0.811 & 0.805 \\ \hline KNN [59] & 0.789 & 0.792 & 0.790 & 0.787 \\ \hline LSTM [64] & 0.843 & 0.872 & 0.857 & 0.864 \\ \hline RCNN [63] & 0.751 & 0.738 & 0.744 & 0.700 \\ \hline CNN [65] & 0.755 & 0.753 & 0.753 & 0.748 \\ \hline RI-CNNA & 0.853 & 0.853 & 0.853 & 0.852 \\ \hline SI- CNNA & 0.859 & 0.842 & 0.850 & 0.852 \\ \hline NS-CNNA & 0.861 & 0.865 & 0.862 & 0.863 \\ \hline
2C-CNNAT & 0.871 & 0.861 & 0.865 & 0.877 \\ \hline
4C-CNNAT & 0.901 & 0.894 & 0.897 & 0.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 4C-CNNAT performance comparison with various ML/DL classifiers using RUSAD corpus. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline RF [82] & 0.725 & 0.726 & 0.725 & 0.716 \\ \hline LR [83] & 0.729 & 0.730 & 0.729 & 0.698 \\ \hline SVM [58] & 0.746 & 0.761 & 0.753 & 0.744 \\ \hline NB [84] & 0.725 & 0.731 & 0.727 & 0.712 \\ \hline KNN [59] & 0.708 & 0.719 & 0.713 & 0.696 \\ \hline LSTM [64] & 0.783 & 0.772 & 0.777 & 0.774 \\ \hline RCNN [63] & 0.751 & 0.738 & 0.744 & 0.700 \\ \hline CNN [65] & 0.742 & 0.741 & 0.741 & 0.732 \\ \hline RI-CNNA & 0.803 & 0.783 & 0.792 & 0.792 \\ \hline SI- CNNA & 0.824 & 0.822 & 0.822 & 0.802 \\ \hline NS-CNNA & 0.829 & 0.825 & 0.826 & 0.813 \\ \hline
2C-CNNAT & 0.841 & 0.841 & 0.841 & 0.847 \\ \hline
4C-CNNAT & 0.851 & 0.854 & 0.852 & 0.853 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 4C-CNNAT performance comparison with various ML/DL classifiers using RUSA-19 corpus. learning. The prior mentioned foundation for equitable correspondence was utilized in the experimentation of the transfer learning process. Then, 10% of the training data, along with ten-fold cross-validation, was utilized as the test set. Each experiment was repeated five times with identical parameters to achieve stable and accurate results. All of the parameters were kept constant except the filter size to emphasize the effects of filter size in the experimentation process. Consequently, filters (4,5,6) demonstrated a successful outcome for all datasets. Similarly, all of the parameters were kept constant except for the number of filters, to emphasize the effects of the number of filters in some experiments. During experimentation, the number of filters was attuned to 300 on DRU and RUDS, along with a dropout rate of 0.6, to achieve the highest classification accuracy. However, the optimal number of filters to achieve maximal accuracy on DRU was 128, along with a dropout rate of 0.5. A number of activation functions were utilized to achieve the finest outcomes, including SoftMax, ReLU, SoftPlus, and Tanh, but ReLU surpassed the performance of all other activation functions due to its faster convergence rate. Figure 5a shows the accuracy of the source dataset with different activation functions. Figure 5b shows the change in accuracy with different dropout rates. Tables 5-8 shows the performance of proposed model in comparison to the baseline ML/DL models. To compare the results of the learning process (Figure 2), firstly, the proposed model was employed for sentiment analysis without prior training on the target domain. In the second phase, using the above-mentioned attuned configurations, the proposed model was incrementally trained on the target domain.