## 4 Results and Discussion  
The proposed CNN-LSTM model for Roman Urdu sentiment analysis is trained on various datasets, as shown in Table 2, then the performance of the proposed model is assessed using various evaluations such as accuracy, precision, recall, and F1-score. Each dataset is divided into testing and training sets. For training, 80% of the data were used, and 20% were used for testing, for each corpus. The excellence of the word-to-vector models and feature extraction methods is ultimately determined by the model performance. Therefore, employing NB KNN, RF, LR, softmax, and SVM classifiers as classification functions after the fully connected layers, we comprehensively evaluated the results of the proposed CNN-LSTM Roman Urdu sentiment analysis. The SVM model outperformed all other used classifiers in terms of accuracy, precision, recall, and F1-score, which is consistent with [32]. Tables 3-5 present the achieved results of our proposed model on UCL, RUSA-19, and the RUSA corpus, respectively. The proposed CNN-LSTM model achieved accuracies of 0.740, 0.748, 0.841, and 0.904 on UCL, RUSA-19, RUSA and IMDB datasets, respectively. On the other hand, the CNN-LSTM performance remained slightly low with the softmax function. Similarly, the performance of proposed model with KNN (K = 10) and NB as classifier functions remained comparatively better than with the softmax function. LSTM, however, is a deep feed-forward neural network model. It has been established that the number of LSTM layers has an effect on classification performance. In the supposed CNN-LSTM Roman Urdu sentiment analysis model, we observed the performance using two LSTM layers vs. one LSTM layer, with 128 units in each LSTM layer. Table 6 summarizes the findings of this investigation. Two stacked LSTM layers increased classification outcomes by +2.10% in accuracy, 1.80% in precision, and 0.90% in recall, compared to a single-layer LSTM. Similarly, our proposed model achieved slightly better results with two-layer LSTM for all used datasets. As a result, two LSTM layers are considered adequate for constructing higher-order feature representations of Roman Urdu phrases so that they may be more easily classified. The proposed model achieved accuracies of 0.719, 0.740, \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Positive** & **Negative** & **Neutral** & **Total** \\  & **Reviews** & **Reviews** & **Reviews** & **Reviews** \\ \hline RUSA-19 & 3778 & 2941 & 3302 & 10,021 \\ \hline UCL & 6013 & 5286 & 8929 & 20,228 \\ \hline RUSA dataset & 5686 & 5314 & - & 11,000 \\ \hline IMDb movie reviews & 25,000 & 25,000 & - & 50,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of used datasets. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**ML Classifier** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** \\ \hline Random Forest & 0.712 & 0.725 & 0.690 & 0.707 \\ \hline Logistic & & & & \\ Regression & 0.706 & 0.700 & 0.695 & 0.697 \\ \hline SVM & 0.740 & 0.750 & 0.714 & 0.731 \\ \hline NB & 0.716 & 0.731 & 0.690 & 0.709 \\ \hline KNN & 0.710 & 0.714 & 0.670 & 0.691 \\ \hline Softmax & 0.695 & 0.690 & 0.673 & 0.681 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Roman Urdu CNN-LSTM performance with various ML classifiers using UCL corpus. and 0.839 with one-layer LSTM against UCL, RUSA-19, and RUSA datasets, respectively. On the other hand, slightly better accuracies of 0.740, 0.748, and 0.841 were achieved by applying two-layer LSTM on UCL, RUSA-19, and RUSA datasets, respectively. Various fixed and pre-trained word embedding techniques such as Word2 Vec, GloVe, TF-IDF, and FastText, were used to test the classification results of the proposed CNN-LSTM Roman Urdu SA. Table 7 compares the accuracy of Word2 Vec (skip-gram and CBOW), GloVe, TF-IDF, and FastText CNN-LSTM models. According to the findings, Word2 Vec-based approaches (CBOW and skip-gram) outperformed FastText, GloVe, and TF-IDF with accuracies of 0.841 and 0.837 for the RUSA dataset.