## 5 Performance Evaluation
### Character-Level CNN-LSTM  
In experiments 1 to 9, described in Table 5, we trained the architectures of character-level CNN-LSTM of different hyperparameters with the cleaned and imbalanced version of the training dataset and evaluated their performance on the test dataset. The number of epochs was set to 50 for all the experiments. For the hyperparameters of CNN and LSTM in experiment 1, we set the number of filters of each CNN convolution layer to 100, the number of neurons to 60, and the dropout rate to 0.5 for LSTM. This is the initial version of the architecture (Figure 3). The best performance of experiment 1 on the test set was a 85.84% accuracy and 28.08% loss in the 49 th epoch with a runtime of 24 min 40 s. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Test Accuracy** & **Test Loss** & **No. of Epochs** & **Runtime** & **Best Accuracy** & **Worst Loss** \\ \hline
3 conv. layers of 256 filters, 2 dense layers of 512 neurons, dropout 0.5 & 0.2582 & 50 & 78 min 57 s & 0.8884 at 47 th epoch & 0.2661 at 47 th epoch \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experiments and results of character-level CNN (using over-sampled training dataset). Figure 4: Evaluation metrics for 50 epochs of training and testing with 3 convolutional layers of 256 filters, 2 dense layers of 512 neurons, and dropout rate of 0.5 using imbalanced training dataset: (**a**) Accuracy and (**b**) Loss. Figure 5: Evaluation metric for 50 epochs of training and testing with 3 convolutional layers of 256 filters, 2 dense layers of 512 neurons, and dropout rate of 0.5 using over-sampled training dataset: (**a**) Accuracy and (**b**) Loss. In experiments 2 and 3, we reduced the dropout rate to 0.2 and 0.4, respectively, from the initial version of the architecture. The model in experiment 2 performed better in terms of accuracy (86.08%) compared to experiments 1 and 3. For experiment 4, we modified the hyperparameters with the number of filters as 256, the number of neurons as 60, and the dropout rate as 0.2. The model in experiment 4 performed better in terms of accuracy (86.44%) compared to the three previous experiments, but its runtime was higher (54 min 18 s). In experiment 5, we increased the dropout rate to 0.5 from the architecture in experiment 4, but did not notice any improvement in the results. Moreover, we modified the hyperparameters with the number of filters as 100, the number of neurons as 100, and the dropout rate as 0.2 for experiment 6 and 0.5 for experiment 7, but also did not see any improvement in both experiments. Furthermore, in experiment 8, we modified the hyperparameters with the number of filters as 256, the number of neurons as 100, and the dropout rate as 0.5, but still did not see any improvement in the results. In experiment 9, we decreased the dropout rate to 0.2 from the architecture in experiment 8. The model in experiment 9 performed the best in terms of accuracy (86.57%) compared to all the previous experiments, with a runtime of 60 min 8 s. The graphs of accuracy and loss for 50 epochs of training and testing from experiment 9 are shown in Figure 6.