# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
###### Abstract
Many researchers have explored the combining of various deep learning techniques with fuzzy logic. Evidence shows that fuzzy logic can be combined with different types of deep neural networks, such as convolutional neural networks (CNN) and recurrent neural networks. This type of combination has been used successfully in various fields, such as image processing, time-series prediction, and various NLP tasks [22]. Different works are targeted at NLP, such as text summarization [23], sentiment analysis, and opinion mining. We focus on only presenting studies that used this combination for the purpose of sentiment analysis. Attempts to combine fuzzy logic with deep neural networks have produced remarkable results in sentiment analysis [24]. These attempts have often proved that this combination is beneficial. A system based on sentiment analysis, called the Senti-eSystem, was proposed in [25]. This system is a hybridization of fuzzy logic and BiLSTM, and it was used to examine satisfaction in customer feedback. The proposed system combined BiLSTM and fuzzy logic in a sequential fashion. BiLSTM provides the sentiment polarity from the feedback, and then fuzzy logic is applied to determine customer satisfaction based on the sentiment class. Using the same combination approach, the authors of [26] analyzed the sentiments in aspects of customer reviews about mobile phones. They introduced a system that successively combined LSTM with fuzzy logic to generate sentiment classes for various aspects of mobile phones. Using a parallel structure, a classifier model that combines fuzzy and deep learning was suggested by the authors of [27]. The classifier model combines CNN and fuzzy logic. The objective of using fuzzy logic was to classify inputs that have two sentiment scores for negativity and positivity into three classes by adding a neutral label. Two Twitter-based datasets were chosen to demonstrate the performance of the model. Several experiments on different aspects were performed to prove the effectiveness of the developed model, and these experiments yielded good results. Conversely, the authors of [9; 24] suggested a hybrid model with a cooperative structure by using deep learning with fuzzy logic at the feature point. The proposed model in [9] integrates the abilities of fuzzy logic with LSTM, and it works by fuzzifying the features to use them as input into LSTM. The authors aimed to predict the sentiments of movie reviews in an IMDB dataset. Similar to this model, the authors of [24] proposed a hybrid model incorporating fuzzy logic with CNN for text sentiment classification. Both models were able to handle the problems of ambiguity and uncertainty in the addressed data and gain better classification accuracies. These works have given us a good start and improved our vision to develop a useful model using fuzzy logic and a deep neural network for the analysis of the sentiments of students' opinions about e-learning, which are in the form of Arabic texts written in the Saudi dialect, in an informal way. In Table 2, we summarize the studies discussed in the previous three paragraphs by highlighting the type of deep learning network used in combination with fuzzy logic, the type of dataset and its language, and the accuracy of the performance achieved by the proposed models. Based on what we previously discussed in detail, there are no works aimed at using fuzzy logic with deep learning targeting the Arabic language. However, we note that most of the contributions in the field of sentiment analysis about e-learning in Saudi Arabia, which has been widely applied recently, dealt only with analyzing the opinions of the public. Moreover, student feedback, which is considered a vital element of this issue, was not given attention by these studies. As a result of this study, several main contributions can be highlighted as follows:  
* A dataset is built from Twitter, and it includes the opinions of Saudi students about e-learning; these opinions are manually annotated as positive or negative.