## 4 Experiments and Results
### Baseline Methods
#### 4.3.2 Short Tweet Analysis Results  
Tables 7 and 8 show the results obtained for two short tweet datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Class** & **Recall** & **Precision** & **F1** & **Accuracy** \\ \hline \multirow{3}{*}{SS-BED} & Pos & 0.8351 & 0.8964 & 0.8633 & \multirow{3}{*}{0.8684} \\  & Neg & 0.9017 & 0.8476 & 0.8728 & \\ \multirow{3}{*}{ARC} & Pos & 0.8184 & 0.9115 & 0.8615 & \\  & Neg & 0.9194 & 0.8365 & 0.8754 & \\ \multirow{3}{*}{IWV} & Pos & 0.8292 & 0.9092 & 0.8664 & \multirow{3}{*}{0.8725} \\  & Neg & 0.9159 & 0.8443 & 0.8779 & \\ \multirow{3}{*}{AC-Bi-LSTM} & Pos & 0.8280 & 0.9253 & 0.8736 & \\  & Neg & 0.9327 & 0.8449 & 0.8864 & \\ \multirow{3}{*}{ABCDM} & Pos & 0.8701 & 0.9387 & 0.9029 & \\  & Neg & 0.9428 & 0.8791 & 0.9097 & \\ \multirow{3}{*}{DNN–} & Pos & 0.8777 & 0.9411 & 0.9092 & \\ \cline{1-1}  & Neg & 0.9482 & 0.8821 & 0.9127 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results obtained for the Electronic dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Class** & **Recall** & **Precision** & **F1** & **Accuracy** \\ \hline \multirow{3}{*}{SS-BED} & Pos & 0.9470 & 0.9403 & 0.9436 & \multirow{3}{*}{0.9100} \\  & Neg & 0.7658 & 0.7913 & 0.7772 & \\ \multirow{3}{*}{ARC} & Pos & 0.9578 & 0.9460 & 0.9518 & \\  & Neg & 0.7870 & 0.8309 & 0.8070 & \\ \multirow{3}{*}{IWV} & Pos & 0.9369 & 0.9367 & 0.9355 & \\  & Neg & 0.7489 & 0.7861 & 0.7542 & \\ \multirow{3}{*}{AC-Bi-LSTM} & Pos & 0.9503 & 0.9459 & 0.9480 & \\  & Neg & 0.7888 & 0.8061 & 0.7693 & \\ \multirow{3}{*}{ABCDM} & Pos & 0.9574 & 0.9520 & 0.9545 & \\  & Neg & 0.8112 & 0.8369 & 0.8209 & \\ \multirow{3}{*}{DNN–} & Pos & 0.9608 & 0.9588 & 0.9603 & \\ \cline{1-1}  & Neg & 0.8167 & 0.8411 & 0.8261 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results obtained for the Airline Twitter dataset.  
Figure 8: The training accuracy of the Airline tweet dataset.  
Figure 6: The training accuracy of the CD dataset.  
Figure 7: The training loss of the Airline tweet.  
We also evaluated our DNN-MHAT model using a dataset from a different language. We ran the DNN-MHAT model on the ASTD [55] dataset in the Arabic language. For a fair comparison, we embedded sentences using AraBERT. Table 9 shows the performance of our model, which achieved a better result.  
To illustrate the performance of our proposed DNN-MHAT model, we executed our model using different embedding layer sizes, namely, 50,100, 200, and 300, as shown in Figures 9 and 10.  
The various embedding sizes have a certain effect on the proposed model's performance, so the DNN-MHAT model's accuracy was evaluated on two datasets when the  
Figure 10: The accuracy of the Airline tweet dataset on various embedding sizes with different epochs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy\%** \\ \hline Arabic-BERT Base [56] & 71.4 \\ AraBERT [57] & 92.6 \\ Arabic BERT [58] & 91 \\ Our model & 92.8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The accuracy of AraBERT DNN–MHAT for ASTD Arabic dataset.  
Figure 9: The accuracy of the CD dataset on various embedding sizes with different epochs.  
number of epochs is equal to 5, 6, 7, 8, 9, and 10. As we can see in Figures 9 and 10, the embedding size of 300 performs better than the other embedding sizes in both CD and Airline datasets.  
The experiments illustrate that the GloVe pre-trained embedding, especially when the embedding size is set to 300, can achieve better results than other embedding sizes.