## 4 Experiments and Analysis
### Results Analysis and Discussion
#### 4.4.4 Analysis of Results on the SDAA Dataset  
Table 6 presents the same comparison of models as we have evaluated in Table 5; however, in this evaluation, we utilized our dataset SDAA, which has never been used previously. The DL models almost gained 92.07% average accuracy on our newly SDAA dataset, similar to the Sentiment14-18k dataset. However, the machine learning classifier with the TF-IDF model secured 82.82% average accuracy with the SDAA dataset, 1.58% higher than \begin{table}
\begin{tabular}{l l c} \hline \hline
**Author/Year** & **Models** & **Accuracy** \\ \hline [PERSON] et al. [53] & CharSCNN & 0.865 \\ [PERSON] et al. [46] & BERT-DNN & 0.81 \\ [PERSON] et al. [38] & CRNN & 0.9205 \\ [PERSON] et al. [55] & HAN & 0.9035 \\ [PERSON] et al. [27] & Glove-DCNN & 0.839 \\ [PERSON] \& [PERSON] [43] & ARC & 0.9229 \\ [PERSON] et al. [36] & IVM & 0.8985 \\ [PERSON] et al. [41] & SS-BED & 0.91 \\ [PERSON] \& [PERSON] [42] & AC-BiLSTM & 0.9172 \\ [PERSON] et al. [34] & TF-IDF-CNN & 0.6879 \\ [PERSON] et al. [34] & TF-IDF-RNN & 0.6174 \\ [PERSON] et al. [34] & Word embeding-CNN & 0.8236 \\ [PERSON] et al. [34] & Word embededing-RNN & 0.8376 \\ [PERSON] et al. [44] & ABCDM & 0.9275 \\ [PERSON] et al. [48] & EFL & 0.9208 \\ \hline  & TF-IDF-Glove-LSTM & 0.9041 \\  & TF-IDF-Glove-BiLSTM & 0.913 \\  & TF-IDF-Glove-CNN & 0.9198 \\  & TF-IDF-Glove-LSTM+CNN & 0.9189 \\  & TF-IDF-Glove-BILSTM-CNN & 0.9358 \\ Our model & ACL-SA & 0.9401 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: US-Airline Twitter dataset accuracy compression with baseline models. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Models** & **Accuracy** & **Average Accuracy** \\ \hline TFIDF with DT & 0.7568 & \\ TFIDF with RF & 0.8589 & \\ TFIDF with SVM & 0.8621 & \\ TFIDF with NB & 0.7721 & \\ \hline TF-IDF-Glove-LSTM & 0.9075 & \\ TF-IDF-Glove-BiLSTM & 0.9090 & \\ TF-IDF-Glove-CNN & 0.9141 & 0.9194 \\ TF-IDF-Glove-LSTM-CNN & 0.9369 & \\ TF-IDF-Glove-BILSTM+CNN & 0.9370 & \\ \hline ACL-SA & 0.9443 & 0.9443 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sentiment140-MV accuracy compression with baseline models and our proposed models. Sentiment140-MV. In contrast, our proposed model's accuracy for our constructed data is 94.53%, sufficiently higher than the existing neural networks. For simplicity, Table 7 and Figure 2 summarize the proposed model's accuracy with the aforementioned neural networks and datasets. It depicts that the proposed ACL-SA model successfully attained the highest accuracy with our structured dataset. In comparison, the average accuracy comparison of our proposed model with the existing models for all datasets is differentiated with the following inequality expression:  
Proposed Model (92.52) > TF-IDF-Glove-BILSTM+CNN (91.48) > TF-IDF-Glove-LSTM+CNN (90.82) > TF-IDF Glove-CNN (89.67) > TF-IDF Glove-BiLSTM (89.18) > TF-IDF Glove-LSTM (88.72). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Models** & **Accuracy** & **Average Accuracy** \\ \hline TFIDF with DT & 0.7391 & \\ TFIDF with RF & 0.8592 & \\ TFIDF with SVM & 0.8621 & \\ TFIDF with NB & 0.8525 & \\ \hline TF-IDF Glove-LSTM & 0.9141 & \\ TF-IDF Glove-BiLSTM & 0.911 & \\ TF-IDF Glove-CNN & 0.9209 & 0.9207 \\ TF-IDF Glove-LSTM-CNN & 0.9238 & \\ TF-IDF Glove-BILSTM+CNN & 0.9337 & \\ \hline ACL-SA & 0.9453 & 0.9453 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SD4A accuracy compression with baseline models and our proposed models. Figure 2: Comparative analysis of the proposed model and model variation on different datasets. Furthermore, to verify the significance of the experimental results obtained by this study, the non-parametric tests are used with a confidence interval of 95% in terms of classification accuracy.