## 5 Discussion and Additional Comparisons  
In addition, we demonstrate the superiority of our model by comparing its detection accuracy with that obtained from other classification methods in comparable investigations, as discussed in Table 4.  
[PERSON] in 2017 used linear SVM classification of sentiment in tweets about airlines. The accuracy result was 78%, and the author suggested that achieving a higher score with more tuning or a more advanced approach is possible [42]. [PERSON] in 2019 explored the Twitter US Airline sentiment dataset and attempted to predict tweet sentiment using a language model and RNN via Fast.ai's library for ULMFiT.  
This model achieved an accuracy of 77.8% [43]. [PERSON] in 2019 performed sentiment analysis with LSTM and CNN. The accuracy of the LSTM and CNN model was 79.64%. [PERSON] in 2019 predicted the Twitter US Airline Sentiment dataset with Multinomial Naive Bayes (NB). Multinomial NB is a good method for text mining and is useful to set a benchmark for sentiment analysis. This technique is easy to implement with sklearn's \"MultinomialNB\" class [36]. Table 5 and Figure 7 show additional performance comparisons for ULMFiT-SVM based on different datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Used Model** & **Accuracy** \\ \hline Twitter US Airlines [30] & SVM only [33] & 78\% \\  & RNN\(/\)LSTM (ULMFiT) [34] & 77.8\% \\  & LSTM, CNN [35] & 79.64\% \\  & MultinomialNB [36] & \(\pm\)80\% \\  & ABCDM [37] & \(\pm\)92.75\% \\  & ULMFit-SVM (Ours) & 99.78\% \\ \hline IMDB [31] & ToWE-SG [38] & 90.8\% \\  & ULMFiT [7] & 95.4\% \\  & BERT large fine-tune UDA [39] & 95.8\% \\  & RCNN [40] & 84.70\% \\  & ULMFit-SVM (Ours) & 99.71\% \\ \hline GOP Debate [32] & SIS-ULMFiT [41] & 55.034\% \\  & ULMFit-SVM (Ours) & 95.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparisons for ULMFiT-SVM-based Twitter US Airlines, IMDB, and GOP debate datasets with several related approaches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameter Name** & **Meaning** & **The Best Value** \\ \hline em-sz & Embedding vector size & 0.77 \\ \hline nh & Hidden activations number & 0.000005 \\ \hline nl & Number of layers & 3 \\ \hline bs & Batch size & 32 \\ \hline \(\beta 1\) & Optimal bias & 0.8 \\ \hline \(\beta 2\) & Optimal bias & 0.99 \\ \hline C-GAMMA & SVM parameters & 5.6569–1.0667 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tested values of hyper-parameters for our model for our sentiment model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Type** & **Accuracy Rate** \\ \hline Twitter US Airlines [30] & 99.78\% \\ IMDB [31] & 99.71\% \\ GOP Debate [32] & 95.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Additional performance comparisons for ULMFiT-SVM based on different datasets.  
Moreover, Table 6 shows that our proposed technique can decrease the training and SVM testing time, which is essential for evaluating the efficiency of sentiment detection systems.  
We also report the F1-score for our model. The F1-scores achieved by ULMFit-SVM in are 99.01%, 98.67%, and 95% on the Twitter US Airlines, IMDB, and GOP debate datasets, respectively.