# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
###### Abstract
The authors of [13; 14; 15] performed an analysis of the sentiment of Saudi tweets, and they used a particular type of deep learning technique, that is, the recurrent neural network such as the GRU and BiLSTM. Contrastingly, there is inherent ambiguity in the natural language of unstructured data, and it cannot be addressed using only deep learning techniques due to their fully deterministic nature making them unable to reduce uncertainties in data [16]. Thus, deep \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & \begin{tabular}{c} **Machine Learning** \\ **Techniques** \\ \end{tabular} & \begin{tabular}{c} **Feature Extraction** \\ **Methods** \\ \end{tabular} & \
\begin{tabular}{c} **Type of Dataset** \\ **Annotation** \\ \end{tabular} &
\begin{tabular}{c} **Language of** \\ **Dataset** \\ \end{tabular} \\ \hline \multirow{2}{*}{[4]} & Logistic Regression, & \multirow{2}{*}{Bag of words} & \multirow{2}{*}{N/A} & \multirow{2}{*}{Arabic} \\  & Support Vector Machine & & & \\ \hline \multirow{4}{*}{[10]} & Logistic Regression, & \multirow{4}{*}{N/A} & \multirow{4}{*}{Automatic} & \multirow{4}{*}{Arabic} \\  & K-Nearest Neighbor, & & & \\ \cline{1-1} \cline{3-5}  & Naive Bayes, & & & \\ \cline{1-1} \cline{3-5}  & Multinomial Naive Bayes, & & & \\ \cline{1-1} \cline{3-5}  & Support Vector Machine & & & \\ \hline \multirow{4}{*}{[11]} & Naive Bayes, & \multirow{4}{*}{N/A} & \multirow{4}{*}{Automatic} & Arabic in Saudi \\  & Random Forest, & & & \\ \cline{1-1} \cline{3-5}  & K-Nearest Neighbor & & & \\ \cline{1-1} \cline{3-5}  & Support Vector Machine, & & & \\ \cline{1-1} \cline{3-5}  & Random Forest, & & & \\ \cline{1-1} \cline{3-5}  & K-Nearest Neighbor & N-Gram, & & \\ \cline{1-1} \cline{3-5}  & Naive Bayes, & TF-IDF & & \\ \cline{1-1} \cline{3-5}  & Logistic Regression, & & & \\ \cline{1-1} \cline{3-5}  & XGBoost & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent studies related to the topic and type of the dataset in this study. learning techniques face challenges because of the vagueness and uncertainties within many written opinions [7]. Furthermore, it can be said that extracting features from Arabic texts and then classifying and analyzing them are challenging tasks; this is due to the lack of contextual information and explicit opinion words in texts, where Arabic texts have a rich morphology, many irregular forms, and a variety of dialects [17; 18]. Addressing and managing these uncertainty and ambiguity issues is a challenging and highly critical task when using sentiment analysis techniques [7]. Considering this importance, in this study we aimed to develop a hybrid model for analyzing the sentiment of student feedback, written in Saudi dialectal Arabic, about e-learning. This hybrid model combines fuzzy logic with BiLSTM, which is a type of deep neural network. Although fuzzy logic has been utilized in several ways by previous works to solve the uncertainty problems in textual feedback analysis based on sentiment in terms of the English language, to the best of our knowledge, no clear efforts have been made in terms of the Arabic language. Apparently, the use of fuzzy logic with the deep learning of an Arabic sentiment analysis has not yet been explored. Most efforts have only targeted the use of fuzzy logic with a lexicon-based approach. [PERSON] et al. [19] and [PERSON] et al. [20] proposed a lexicon-based approach using fuzzy logic to predict the sentiment of texts written in the Arabic language. The proposed model in this study is a hybridization of fuzzy logic and a deep neural network. The main purpose of the proposed model is to incorporate the advantages of both these techniques, as they complement each other. This hybridization combines the approximate reasoning ability of fuzzy logic with the learning capability of deep neural networks. A combination of deep neural networks and fuzzy logic can produce a more reliable and accurate output, as fuzzy logic enhances the generalization capability of neural networks [16; 17; 18; 19; 20; 21].