# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 6 Discussion  
This work combined fuzzy logic with BiLSTM to develop a hybrid model. We used this model to analyze the sentiment of students' feedback about e-learning. This feedback was obtained from tweets posted by students expressing their opinions about e-learning in Saudi Arabia. Inherently, this collected feedback had ambiguous characteristics. We discuss these ambiguous characteristics in the next paragraph.  
Normally, expressions of opinions written naturally by humans are texts that contain a lot of vagueness and noise [51; 52; 53]. The opinion orientation of personal text differs depending on the context or domain in which it is expressed. Moreover, the language of the feedback is Arabic and written in an informal way without writing standards, using different Saudi dialects. This means that these texts have a rich morphology and orthography [17; 18]. The opinion terms are vague in nature and have unclear boundaries, and the diversity of dialects may lead to the meaning of these terms being interpreted differently. Textual data that possess all the above ambiguities impose significant difficulties in processing, feature extraction, and word representation, as well as in classification.  
The findings confirm that our model was able to yield good results, and they also reveal that our model outperformed the selected baseline models. There are essential and significant interpretations of these results. The feature extraction process is the backbone impacting the performance of a model. Essentially, better feature extraction leads to better classification and prediction. In fact, the proposed model has an efficient architecture for the processing, feature extraction, and classification of textual data. We added two fuzzy layers, along with BiLSTM. We configured the fuzzify layer directly after the embedding layer that extracts the features. The fuzzify layer is in the middle, between the embedding layer and the BiLSTM layer. This fuzzify layer, with its membership functions, provides an effective additional procedure for extracting features and making them more separable. It reduces the potential vagueness of the data representation resulting from the feature extraction. Then, the output extracted features are more distinguishable for the subsequent layers.  
Therefore, we can confirm that the proposed model worked well in addressing uncertainty issues. Thus, it makes an important contribution to analyzing texts written in the Arabic language. Evidently, combining fuzzy logic with the existing BiLSTM model afforded a further enhancement of the feature extraction and text classification, hence obtaining more accurate results. In addition, it provided better performance and allowed us to classify with higher accuracy than the standalone BiLSTM model.  
The annotation process for the dataset represented an unexpected challenge. It was costly, consumed time and human effort, and required annotators who had knowledge of the Saudi dialects and experience with the annotation process. Consequently, the annotation process was not completed for the desired size of the dataset. This challenge produced the first limitation of this research, which was the limited size of the data. Therefore, in the future, we plan to investigate these dataset annotation challenges and increase the dataset size by completing the annotation process.  
In the future, we intend to carry out an in-depth comparative study between our model and a set of transformers. Moreover, we will investigate the possibility of combining fuzzy models with a suitable transformer. Also, we plan to apply our model to different Arabic datasets.  
[PERSON] and F.F. contributed equally to all parts of this work. All authors have read and agreed to the published version of the manuscript. This work was funded by the Deanship of Scientific Research at Qassim University, Saudi Arabia, under number (COC-2022-1-2-J-29742), during the academic year 1444 AH/2022 AD.  
**Institutional Review Board Statement:** Not applicable.