## 2 Related Work  
One of the main applications of data analysis is making sense of the unstructured text, such as open-ended responses in course evaluation questionnaires. NLP [18], with the help of other text mining methods [19; 20; 21], is making this kind of analysis more accessible. In the following discussion, we will highlight some of the progress made in the literature for open-ended student feedback. Lexicon-based sentiment analysis has been used for open-ended students' feedback to identify their positive or negative attitudes toward the learning materials [8]. The method involves analyzing the intensifier words extracted from students' feedback to determine the polarity of words by mapping them against a database of English sentiment words. Similarly, in 2020, a customized lexicon-based analysis tool was designed to explore and analyze the students' reflective journal writing [22]. [PERSON] et al. [10] proposed a hybrid approach that combines machine learning and lexicon-based techniques to perform sentiment analysis on students' textual feedback. The study uses a training set of more than 1000 open-ended student feedback and a lexicon-based analysis combined with a Term Frequency-Inverse Document Frequency (TF-IDF) feature to determine the essential words and assign sentiment scores to the words. The computed sentiment scores were used to train a set of classifiers to predict the sentiment scores of previously unseen students' feedback. [PERSON] et al. [23] proposed a fuzzy logic-based method to determine students' satisfaction with their open-ended feedback. The study assigns sentiment scores to opinion words and polarity shifters extracted from the students' responses. The generated scores are then analyzed using fuzzy logic to quantify the students' satisfaction. [PERSON] et al. [11] present an aspect-level sentiment prediction model for course evaluation of students' feedback. The study trained three deep-learning models to predict the sentiment score in open-ended student feedback. Thanks to their large dataset of more than 4000 comments, the study achieved good results with the trained model using a topic dictionary as an input and attention mechanism. Although the proposed work could give the sentiment score of each student's comment individually, no global teaching evaluation was computed by correlating the students' assessments with each other. Some studies also examine the students' emotions [9; 22; 24]. For example, [PERSON] et al. [9] proposed a study to assess open-ended student feedback by focusing on emotional analysis. First, they performed sentiment analysis to find the positive, negative, and neutral texts. Then, they used the NRC lexicon [25] to calculate the emotion values for each feedback and classify emotions among anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. Finally, they visualized the results using the word cloud library. [PERSON] et al. [24] proposed a method that uses an educational process, data mining, and a machine learning model to analyze the feedback from students' evaluation of teaching. The study extracts the sentiments and emotions expressed by the students and then performs the covariance and the Kruskal-Wallis Test to understand the influential factors in the students' comments. [PERSON] et al. [22] proposed an NLP model that applies a lexicon-based classification system to categorize the reflection level of reflective writings, followed by a fuzzy-based approach to increase the model's accuracy. As a result, the model identified the following reflection levels: description, feelings/emotions, relation, reasoning, and critical reflection. For this purpose, a specific reflective keyword dictionary was created using the WordNet library. In addition, the model offers a user-friendly layout to visualize the classification in HTML format in any browser. Some studies also use [PERSON]'s taxonomy classification [17]. For example, [PERSON] et al. [26] combine [PERSON]'s taxonomy classification with NLP to categorize and evaluate examination questions.