# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 5 Results
### Comparative Results  
The proposed model combines fuzzy logic with the well-known BiLSTM model. In fact, this standalone BiLSTM model has been used in most closely related works, such as [14, 15]. Accordingly, we considered the standalone BiLSTM model as a baseline to be compared with our proposed model in order to demonstrate the efficiency of integrating fuzzy logic with DNN models and to show how this integration contributes to achieving the purpose of this study. For a fair comparison, we set the parameters of the standalone BiLSTM to be the same as those of the proposed model. We also first performed several separate experiments on the standalone BiLSTM model using different configurations. These experiments were carried out on the same dataset and using the same train/test split procedure (80-20%), to determine whether better results could be obtained with the other configurations. The experiments showed that the optimal parameters for our proposed model also gave the best results for the standalone BiLSTM. In Table 8 and Figure 6, only the best results for the standalone BiLSTM and our proposed model are shown. The results show that the performance improved with the proposed model compared to the standard BiLSTM, and it is apparent that our proposed model has the highest values in the obtained results. The proposed model outperformed the standalone BiLSTM by almost 6%. The standalone BiLSTM had an accuracy of 80% and an F1-score of 76%, whereas our proposed model had an accuracy of 86% and an F1-score of 84%.  
Furthermore, we compared the performance of our proposed model with that of four prevalent and well-known machine learning models: naive Bayes (NB), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LR). These models were used in experiments conducted by [11, 12], which can be considered studies that are closely related to our work according to the type of analysis tasks carried out and the types of datasets used. Additionally, we performed a comparison with another prevalent machine learning model, namely, the decision tree (DT) and its fuzzy version (FDT), both of which were utilized in [50]. We have to mention that we have used TF-IDF-based features for learning the traditional machine learning models. Practically, each student's feedback has been converted to a vector of numerical data. Additionally, we carried out our experimental study on the same dataset and using the same train/test split procedure (80-20%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Accuracy** & **F1-Score** & **Recall** & **Precision** \\ \hline Five-fold cross-validation & 0.840 & 0.826 & 0.821 & 0.832 \\ \hline \multirow{3}{*}{Train/test split} & (60–40\%) & 0.853 & 0.845 & 0.861 & 0.831 \\ \cline{1-1}  & (70–30\%) & 0.852 & 0.846 & 0.851 & 0.842 \\ \cline{1-1} \cline{2-5}  & (80–20\%) & 0.861 & 0.851 & 0.870 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of experiments of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Recall** & **Precision** \\ \hline
**Standalone BiLSTM** & 0.804 & 0.767 & 0.790 & 0.747 \\ \hline
**Our Proposed Model** & 0.861 & 0.851 & 0.870 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative results for the proposed model and the standalone BiLSTM.