# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
###### Abstract  
It is crucial to analyze opinions about the significant shift in education systems around the world, because of the widespread use of e-learning, to gain insight into the state of education today. A particular focus should be placed on the feedback from students regarding the profound changes they experience when using e-learning. In this paper, we propose a model that combines fuzzy logic with bidirectional long short-term memory (BiLSTM) for the sentiment analysis of students' textual feedback on e-learning. We obtained this feedback from students' tweets expressing their opinions about e-learning. There were some ambiguous characteristics in terms of the writing style and language used in the collected feedback. It was written informally and not in adherence to standardized Arabic language writing rules by using the Saudi dialects. The proposed model benefits from the capabilities of the deep neural network BiLSTM to learn and also from the ability of fuzzy logic to handle uncertainties. The proposed models were evaluated using the appropriate evaluation metrics: accuracy, F1-score, precision, and recall. The results showed the effectiveness of our proposed model and that it worked well for analyzing opinions obtained from Arabic texts written in Saudi dialects. The proposed model outperformed the compared models by obtaining an accuracy of 86% and an F1-score of 85%. fuzzy logic; sentiment analysis; feature extraction; deep neural networks; deep learning; e-learning +
Footnote †: journal: _Applied_  
that it expresses [6]. Feedback analysis using opinion mining or the sentiment analysis approach revolves around the same meaning and objectives. They all fall within fields of study aimed at identifying the sentiments, appraisals, attitudes, and opinions expressed in human-written texts toward various entities, such as issues, events, and topics [7]. They utilize natural language processing (NLP), text mining, and text feature extraction to classify the polarity of a text as positive or negative [8]. Despite the similarities between opinion mining and the sentiment analysis approach, they are slightly different. The sentiment analysis approach identifies words and phrases that convey emotions, while opinion mining is the process of extracting opinions in general on a particular entity [3]. In this study, we employ the sentiment analysis approach to analyze students' feedback about e-learning. Researchers have used various techniques in the literature to correctly predict the sentiments implied in a text. These techniques started with lexicon-based approaches, then evolved to machine learning techniques, and then advanced to deep learning techniques [9]. Regarding the Arabic sentiment analysis, there are recent studies related to the topic and type of dataset used in this study, i.e., e-learning and Twitter-based data. The authors in [4; 10] performed sentiment analyses of general Arabic tweets about e-learning, while the researchers in [11; 12] analyzed Twitter datasets about e-learning relating to Saudi Arabia. These studies only focused on using traditional machine learning techniques, such as the naive Bayes and random forest techniques, and traditional feature extraction methods, such as the N-Gram and TF-IDF methods. Table 1 provides an overview of the machine learning techniques used in these studies and the methods of feature extraction, as well as information about the dataset language and the type of annotation. Table 1 reveals many gaps related to Arabic Sentiment Analysis. In fact, the most serious gap in this domain is the few numbers of research articles that handle this problem, especially for the Saudi dialects. Machine learning techniques have been enhanced by deep learning techniques, which carry out feature extraction automatically [9]. Therefore, they perform better in NLP tasks, such as text classification. Accordingly, they have shown a great enhancement in the sentiment analysis of texts written in different languages.