## 4 Experimental Result  
Table 6 summarizes the experimental results for each neural network and the transfer model used. These findings are based on the XLNET, BERT, AlBERT, RoBERTa, DistillBERT base, and large variants of the models. The GRU+RoBERTaLARGE model achieved the highest training accuracy of 96%. The training lasted 48 h on a Core i7 CPU 3.60 GHz with 16 GB RAM for the LSTM+ RoBERTaLARGE. Other models trained with lesser time compared to this. The GRU trained faster than the LSTM, but the LSTM showed slightly better accuracy during testing. This is associated with the LSTM model's learning method, allowing it to learn more information than the GRU model. The result was analyzed using the recall, precision, and F1-score metrics. Tables 7 and 8 show the outcomes of testing the model using the Amazon reviews and TM message-reviews datasets, respectively. Table 7 demonstrates that while the scores from all the models are relatively close, the RoBERTa model stands out among the others. This is similar to the result in Table 8. This result shows that the amount of training data used in the pre-training of the RoBERTa model impacts the model's performance. As a result, the model has a stronger grasp of the English language's context. The only significant downside is the length of time required for model training. The training took around three times as long as the other models in our study. In addition, the nature of the English language plays a vital role in the recall value of the classes. The number of times punctuation appears at the end of a word is extremely low compared to the number of times it does not. Therefore, we can see the recall values of the classes COMMA, PERIOD, and QUESTION are low compared to the OTHERS class in all the models. Table 9 summarizes the best model based on the results of the Amazon reviews testing. Figure 4 compares the best-proposed model to those described in other studies. For comparison, the same transcribed dataset utilized in other models was employed in testing our model. All other models were trained using annotated transcribed text, whereas we used social media data to train our model. The result indicates that the training data utilized in our model effectively produced the desired outcome during testing and was comparable to that employed by other researchers. Using our model, we achieved much better results in the COMMA class. As a result, our algorithm is significantly better at predicting com location in a text. When predicting the classes COMMA and QUESTION, LSTM+RoBERTaLARGE has greater precision, recall, and F1 score than the other classes, with values of 0.98, 0.98, and 0.98, respectively. Precision, recall, and F1 score have 0.86, 0.93, and 0.88, respectively, in the QUESTION class. LSTM + RoBERTaLARGE [13] did better to predict the PERIOD class, with precision, recall, and F1 score values of 0.88, 0.92, and 0.91, respectively, compared to LSTM+RoBERTaLARGE, which had precision, recall, and F1 score values of 0.76, 0.74, and 0.75, respectively. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Comma** \\ **P** \\ \end{tabular}  & **R** & **F1