# Roman Urdu Sentiment Analysis Using Transfer Learning
## 5 Results and Discussions  
The proposed model was tested in five discrete configurations to achieve the best possible outcome. CNN-A with Random Input Vector (RI-CNNA): As input, it employs randomly initialized vectors. CNN-A with Static Input Vector (SI- CNNA): As input, it takes Word2 Vec-obtained pre-trained word vectors. There is no iterative updating of weights during the training period. CNN-A with Non-Static-Input Vector (NS-CNNA): Word2 Vec's pre-trained word vectors are used as the input. During training, weights are continuously adjusted. CNN-A with 2 Channel- Input Vector (2C-CNNAT): Word2 Vec's pre-trained word vectors are used with randomly initialized vectors to train the model. CNN-A with 4 Channel- Input Vector (4C-CNNAT): A mixture of Random, Word2 Vec, Glove, and FastText's pre-trained word vectors are used as input. Throughout the course of training, the weights are also adjusted. The proposed model was only trained on the target datasets to create a foundation for equitable correspondence between the proposed model and other ML and DL techniques. The output was measured in the form of accuracy, precision, recall, and f1 score matrices. The detailed comparison of the outputs for each test is presented in Tables 3 and 4. It is evident that the proposed model, in its various forms, outperforms the state-of-the-art by a small margin and is thus a viable option for transfer learning. Using randomly initialized vectors as an input may be why RI-CNNA has the worst classification performance among the presented model variants on both target datasets. The use of pre-trained vectors that are able to address the semantic sparsity problem has been linked to improved performance across a number of variants. Further, as SI-CNNA has the second-lowest classification accuracy after RI-CNNA, it is evident that updating word vectors during training could result in a greater performance regardless of whether the word vectors were previously trained or not. In conclusion, 4C-CNNA achieves 0.853% accuracy in classification on the RUSA-19 dataset and 0.903% accuracy on the RUSAD dataset. As seen in the results, the proposed model, along with its distinct configurations, performs better than the existing techniques. Therefore, it appeared to provide a better performance in the case of transfer learning. Figure 4 depicts a plot of classification accuracy versus loss values per epoch throughout training and validation sets for 4C-CNNA- on the RUSA-19 and RUSAD. As can be seen in the figure, as the number of epochs is raised, classification accuracy increases while the loss value decreases. Moreover, there was a point of stability in the validation loss plot, and the validation loss was only slightly lower than the training loss. In light of this, we can say that the model has been appropriately adjusted and provides a decent fit. Effect of Transfer LearningOne of the disadvantages of CNNs is their variable number of hyper-parameters, which necessitates practitioners to define the precise model architecture. Considering that the values of the hyper-parameters have a substantial impact on the performance of DNN, we chose to improve the proposed model's hyper-parameters on the source domains and then apply the optimal parameters to the target domains while employing transfer