## 4 Results and Discussion
Similarly, Word2 Vec-based models (CBOW and skip-gram) achieved the highest accuracy and F1-score for other corpus \begin{table}
\begin{tabular}{l l l l l} \hline \multirow{2}{*}{**Dataset**} & **No of LSTM** & **Accuracy** & **Precision** & **Recall (\%)** & **F1-Measure (\%)** \\  & **Layers** & **(\%)** & **(\%)** & **(\%)** & **Recall (\%)** & **(\%)** \\ \hline \multirow{2}{*}{UCL} & 1 & 0.719 & 0.732 & 0.705 & 0.718 \\  & 2 & 0.740 & 0.750 & 0.714 & 0.731 \\ \hline \multirow{2}{*}{RUSA-19} & 1 & 0.740 & 0.744 & 0.706 & 0.724 \\  & 2 & 0.748 & 0.762 & 0.729 & 0.745 \\ \hline \multirow{2}{*}{RUSA dataset} & 1 & 0.839 & 0.830 & 0.821 & 0.825 \\  & 2 & 0.841 & 0.850 & 0.840 & 0.844 \\ \hline \end{tabular}
\end{table} 
Table 6: Effect of LSTM layers on proposed Roman Urdu CNN-LSTM model. \begin{table}
\begin{tabular}{l l l l l} \hline \multirow{2}{*}{**ML Classifier**} & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** \\ \hline Random Forest & 0.695 & 0.693 & 0.673 & 0.682 \\ \hline Logistic & & & & \\ Regression & 0.698 & 0.701 & 0.674 & 0.687 \\ \hline SVM & 0.748 & 0.762 & 0.729 & 0.745 \\ \hline NB & 0.729 & 0.735 & 0.694 & 0.713 \\ \hline KNN & 0.708 & 0.719 & 0.675 & 0.696 \\ \hline Softmax & 0.670 & 0.663 & 0.648 & 0.655 \\ \hline \end{tabular}
\end{table} 
Table 4: Roman Urdu CNN-LSTM performance with various ML classifiers using RUSA-19 corpus. \begin{table}
\begin{tabular}{l l l l l} \hline \multirow{2}{*}{**ML Classifier**} & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** \\ \hline Random Forest & 0.770 & 0.770 & 0.762 & 0.765 \\ \hline Logistic & & & & \\ Regression & 0.760 & 0.762 & 0.758 & 0.759 \\ \hline SVM & 0.841 & 0.850 & 0.840 & 0.844 \\ \hline NB & 0.811 & 0.812 & 0.800 & 0.805 \\ \hline KNN & 0.789 & 0.792 & 0.783 & 0.787 \\ \hline Softmax & 0.755 & 0.753 & 0.745 & 0.748 \\ \hline \end{tabular}
\end{table} 
Table 5: Roman Urdu CNN-LSTM performance with various ML classifiers using RUSA dataset. The suggested model's findings against the IMDb movie reviews corpus are shown in the Table 8. Movies that received ratings of 4 or less from viewers were labeled as bad reviews, while those that received ratings of 7 or more were labeled as positive reviews. The reviews of movies within a rating of 7 to 4 were excluded from the corpus. Across a variety of datasets and languages, the suggested model exhibited the same pattern of performance. With two-layer LSTM, our proposed CNN-LSTM model with BERT as an embedding layer obtained the maximum accuracy of 0.904. The BERT embedding model outperforms the others due to the transformer and self-attention technologies. Due to the fixed word-to-vector representations, the suggested model achieved less accuracy with fixed word embedding approaches such as Word2 Vec. Pre-trained word embedding, such as FastText, on the other hand, yielded better outcomes than Word2 Vec, since FastText is pre-trained on the English language. Similarly, the additional LSTM second layers improved the performance of the proposed model. As a classifier function, the SVM model surpassed all other models such as NB, KNN, RF, and LR. We conducted various experiments on various datasets to confirm the results of the CNN-LSTM Roman Urdu SA against existing models. The UCL dataset includes 20,228 user reviews gathered from a variety of social media sources. The RUSA-19 dataset contains 10,021 Roman Urdu user reviews. The RUSA dataset includes 11,000 negative and positive Roman Urdu user reviews gathered from various social media platforms such as YouTube, Facebook, etc. The CNN-LSTM Roman Urdu SA classification accuracy was compared to research study [31], recurrent convolutional neural network and rule-based approaches were used. The study in [32] used various machine learning algorithms such as RF, SVM, DT, NB, KNN, LR, ANN, AdaBoost and wVoting. The research study in [33] implemented various machine learning algorithms such as RF, SVM, DT, NB, KNN, LR, ANN, AdaBoost and wVoting with word-gram and character-gram features union.