## 4 Experiments and Analysis
### Results Analysis and Discussion
#### 4.4.4 Analysis of Results on the SDAA Dataset
Significant differences are found in each pair for the test results obtained of the ACL-SA with CRNN, IWC, ABCDM, and Word Embedding-RNN for the Sentiment140 dataset, Table 3. Similarly, the Wilcoxon Signed-rank test was used in each pair ACL-SA with ABCDM, CRNN, ACR, IVM, SS-BED, and Word Embedding-RNN of the US-airline dataset, Table 4. The results are shown in Table 8. In both Tables 3 and 4, the Wilcoxon test results show an enormously significant difference, and the hypothesis has been validated (\(p\)-value < 0.05) for all methods paired with our proposed method. As mentioned in the literature [40, 45], overfitting, a critical challenge in deep learning models, reduces the model's accuracy and performance. However, this challenging task is resolved by utilizing the Gaussian Noise and Gaussian Dropout on different Twitter datasets. Figure 2 presents the convergence of our proposed model that attained a reliable accuracy based on experiments conducted on training and validation datasets with 50 epochs and a batch size of 256. For instance, in Figure 3a, the SD4A validation data are initiated from an accuracy of 0.814, steadily increasing to 0.935 at epoch 10, and remains constant for all other epochs. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **TF-IDF-** & **TF-IDF-** & **TF-IDF-** & **TF-IDF-Glove** & **TF-IDF-GloVe-** & **ACL-SA** \\  & **Glove-CNN** & **Glove-BiLSTM** & **Glove-LSTM** & **-LSTM+CNN** & **BILSTM+CNN** & \\ \hline SD4A & 0.9209 & 0.911 & 0.9141 & 0.9238 & 0.9337 & 0.9453 \\ US airline & 0.9198 & 0.913 & 0.9041 & 0.9289 & 0.9358 & 0.94 \\ Sentiment140 & 0.8322 & 0.8345 & 0.8234 & 0.8432 & 0.853 & 0.8712 \\ Sentiment140-MV & 0.9141 & 0.909 & 0.9075 & 0.9369 & 0.937 & 0.9443 \\ Average accuracies & 0.8967 & 0.8919 & 0.8873 & 0.9082 & 0.91499 & 0.9252 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the proposed model’s accuracy. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Measure** & **Dataset** & **Comparison** & **Hypothesis** & \(p\)**-Value** \\ \hline \multirow{8}{*}{Classification accuracy} & \multirow{8}{*}{US-airline} & ACL-SA vs. ABCDM & Rejected for ACL-SA & 0.003 \\  & & ACL-SA vs. CRNN & Rejected for ACL-SA & 0.025 \\  & & ACL-SA vs. ACR & Rejected for ACL-SA & 0.025 \\  & & ACL-SA vs. IVM & Rejected for ACL-SA & 0.026 \\  & & ACL-SA vs. SS-BED & Rejected for ACL-SA & 0.024 \\  & & ACL-SA vs. AC-BiLSTM & Rejected for ACL-SA & 0.016 \\  & & ACL-SA vs. Word Embeeding-RNN & Rejected for ACL-SA & 0.031 \\ \hline \multirow{8}{*}{Sentiment140} & \multirow{8}{*}{ACL-SA vs. CRNN} & Rejected for ACL-SA & 0.001 \\  & & ACL-SA vs. IWV & Rejected for ACL-SA & 0.011 \\ \cline{1-1}  & & ACL-SA vs. SAPCP & Rejected for ACL-SA & 0.001 \\ \cline{1-1}  & & ACL-SA vs. Word Embedding-RNN & Rejected for ACL-SA & 0.001 \\ \cline{1-1}  & & ACL-SA vs. ABCDM & Rejected for ACL-SA & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: [PERSON] tests of different sentiment analysis methods in terms of classification accuracies. Meanwhile, the training data accuracy fluctuates from 0.60 and increases steadily to 0.952 with epoch increment. At the same time, it became constant after the 15 th epoch. Similarly, Figure 3 presents the accuracy trend of training and validation of the US airline dataset. It provides a rapid accuracy curve of 10 epochs starting from 0.839 to 0.938 on the validation set. For the training set, it starts from 0.788 to 0.94, which becomes constant after 10 epochs. Figure 3c,d show that the accuracy trends for the sentiment140 and sentiment140-118 k datasets are slightly different from SD4A and US-airline datasets. Figure 3c shows that the accuracy trends for the sentiment140 dataset improved smoothly from 0.75 to 0.89 for epochs 5 to 20. Later, it is constant on average for both training data and validation data. On the other hand, in Figure 3d, we observed that the training and validation results of the Sentiment140-MV dataset could not attain accuracy in epochs 0-5.