## 4 Results and Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Word Embedding** & **Classifier** & **LSTM Layers** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** \\ \hline \multirow{8}{*}{Word2 Vec} & Random Forest & 1 & 0.847 & 0.840 & 0.850 & 0.844 \\  & 2 & 0.853 & 0.847 & 0.854 & 0.850 \\  & Logistic Regression & 1 & 0.851 & 0.845 & 0.855 & 0.849 \\  & 2 & 0.852 & 0.848 & 0.856 & 0.851 \\  & SVM & 1 & 0.854 & 0.848 & 0.859 & 0.853 \\  & 2 & 0.856 & 0.851 & 0.861 & 0.855 \\  & NB & 1 & 0.851 & 0.847 & 0.857 & 0.850 \\  & 2 & 0.855 & 0.848 & 0.860 & 0.853 \\  & KNN & 1 & 0.848 & 0.843 & 0.851 & 0.846 \\  & 2 & 0.850 & 0.845 & 0.853 & 0.848 \\ \hline \multirow{8}{*}{FastText} & Random Forest & 1 & 0.862 & 0.859 & 0.860 & 0.859 \\  & Logistic Regression & 2 & 0.866 & 0.863 & 0.865 & 0.863 \\  & 2 & 0.865 & 0.862 & 0.866 & 0.863 \\  & SVM & 1 & 0.868 & 0.865 & 0.868 & 0.866 \\  & SVM & 1 & 0.871 & 0.869 & 0.871 & 0.869 \\  & 2 & 0.873 & 0.872 & 0.874 & 0.872 \\  & NB & 1 & 0.856 & 0.849 & 0.862 & 0.855 \\  & 2 & 0.861 & 0.853 & 0.866 & 0.859 \\  & KNN & 1 & 0.851 & 0.847 & 0.855 & 0.850 \\  & 2 & 0.856 & 0.849 & 0.858 & 0.853 \\ \hline \multirow{8}{*}{BERT} & Random Forest & 1 & 0.889 & 0.885 & 0.890 & 0.887 \\  & 2 & 0.894 & 0.888 & 0.894 & 0.890 \\ \cline{1-1}  & Logistic Regression & 1 & 0.892 & 0.887 & 0.892 & 0.889 \\ \cline{1-1}  & SVM & 1 & 0.895 & 0.890 & 0.895 & 0.892 \\ \cline{1-1}  & SVM & 2 & **0.904** & **0.895** & **0.903** & **0.898** \\ \cline{1-1}  & NB & 1 & 0.892 & 0.887 & 0.892 & 0.889 \\ \cline{1-1}  & NB & 2 & 0.896 & 0.889 & 0.895 & 0.891 \\ \cline{1-1}  & KNN & 1 & 0.889 & 0.883 & 0.890 & 0.886 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: CNN-LSTM model accuracy using various word embeddings with two-layer LSTM using IMDb movie reviews corpus. as negative. Only 15.40% and 14.10% of neutral reviews were misclassified as positive and negative, respectively, while 70.50% of negative reviews were classified accurately as neutral using CNN-LSTM Roman Urdu sentiment analysis on the UCL corpus. Only 12.20% of positive reviews were mislabeled as negative and 11.68% as neutral, while 76.12% of positive reviews were classified accurately as positive. Only 9.08% and 13.90% of negative reviews were misclassified as positive and neutral, respectively, while 77.02% of negative reviews were classified accurately as negative. Only 14.88% and 13.90%  
Figure 5: Confusion matrix of CNN-LSTM Roman Urdu sentiment analysis with UCL corpus. Figure 6: Confusion matrix of CNN-LSTM Roman Urdu sentiment analysis with RUSA-19 corpus. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Dataset** & \begin{tabular}{l} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{l} **Precision** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Recall** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{l} **F1-Measure** \\ **(\%)** \\ \end{tabular} \\ \hline
[31] & UCL & 0.693 & 0.732 & 0.699 & 0.715 \\  & RUSA-19 & 0.713 & 0.710 & 0.683 & 0.696 \\ \hline
[33] & RUSA dataset & 0.811 & 0.816 & 0.828 & 0.822 \\ \hline
[32] & RUSA dataset & 0.821 & - & - & - \\ \hline Proposed model & UCL & **0.740** & **0.750** & **0.714** & **0.731** \\  & RUSA-19 & **0.748** & **0.762** & **0.729** & **0.745** \\  & RUSA dataset & **0.841** & **0.850** & **0.840** & **0.844** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Proposed CNN-LSTM Roman Urdu model comparison with existing models. Figure 7: Confusion matrix of CNN-LSTM Roman Urdu sentiment analysis with RUSA dataset. of neutral reviews were misclassified as positive and negative, respectively, while 71.22% of negative reviews were classified accurately as neutral using CNN-LSTM Roman Urdu sentiment analysis on the RUSA-19 corpus.