# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 4 Systematic Mapping Study Results
### Findings Concerning RQs  
For the purposes of the analysis, the 92 papers remaining after the exclusion criteria were reviewed in detail by the five authors; in this section, the results are presented in the context of the research questions listed in Section 3. **RQ1**. _What are the most investigated aspects in the education domain with respect to sentiment analysis?_  
Students' feedback is an effective tool that provides valuable insights concerning various educational entities including teachers, courses, institutions, etc. and teaching aspects related to these entities. The identification of these aspects as expressed in the textual comments of students is of great importance as it aids decision makers to take the right action to specifically improve them. In this context, we examined and classified the reviewed papers based on the aspects that concerned students and that the authors aimed to investigate. In particular, we found three categories and their related teaching aspects which were objects of investigation in these papers: the first category comprised studies dealing with the comments of students concerning various aspects of the teacher entity, including the teacher's knowledge, pedagogy, behavior, etc; the second category contained papers concerning various aspects of the three different entities, such as courses, teachers, and institutions. Course-related aspects included dimensions such as course content, course structure, assessment, etc., whereas aspects associated to the institution entity were tuition fees, the campus, student life, etc. ; the third category included papers dealing with capturing the opinions and attitudes of students toward institution entities. The findings illustrated in Figure 6 show that 81% of reviewed papers focused on extracting opinions, thoughts, and attitudes toward teachers, with 6% corresponding to institutions, whereas 13% presented a more general approach by investigating students' opinions toward teachers, courses, and institutions. **RQ2**. _Which approaches and models are widely studied for conducting sentiment analysis in the education domain?_  
Numerous approaches and models have been employed to conduct sentiment analysis in the education domain, which generally can be categorized into three groups. Table 4 shows the papers grouped based on learning approaches that the authors have applied within their papers. In total, 36 (out of 92) papers used a supervised learning approach, 8 used an unsupervised learning approach, and 20 used a lexicon-based approach. Figure 6: Feedback aspects investigated in the reviewed papers. Thus, seven papers used both supervised and unsupervised approaches. Twenty papers used lexicon-based and supervised learning, whereas seven papers used lexicon-based and unsupervised learning. In total, three (out of 92) articles used all three learning approaches as a hybrid approach, in contrast with five other articles, which did not specify any learning approach. Table 5 emphasizes that the Naive Bayes (NB) and Support Vector Machines (SVM) algorithms, as part of the supervised learning approach, were used most often in the reviewed studies, followed by Decision Tree (DT), k-Nearest Neighbor (k-NN) and Neural Network (NN) algorithms. Furthermore, the use of a lexicon-based learning approach, also known as rule-based sentiment analysis, was common in a number of studies as shown in Table 4 and very often associated either with supervised or unsupervised learning approaches. Table 6 lists the most frequently used lexicons elaborated among the reviewed articles, where the Valence Aware Dictionary and Sentiment Reasoner (VADER) and Sentiwordnet were used very often compared to TextBlob, MPQA, Sentistrength, and Semantria. **RQ3. **_What are the most widely used evaluation metrics to assess the performance of sentiment analysis systems?_  
Information retrieval-based evaluation metrics were widely used to assess the performance of systems developed for sentiment analysis. The metrics include the precision, recall, and F1-score.