Quest_SA: Preprocessing Method for Closed-Ended Questionnaires Using Sentiment Analysis through Polarity

Abstract

Sentiment analysis is a prominent research topic in natural language processing, with applications in politics, news, education, product review, and other sectors. Especially in the education sector, sentiment analysis can assist educators in finding students’ feelings about a course on time, altering the teaching plan appropriately and timely to improve the quality of education and teaching. For students, the sentiment analysis can identify emotions, academic performance, behaviour, and so on; the primary purpose of this research paper is to analyze students’ emotions, self-esteem, and efficacy based on closed-ended questionnaires. This paper proposes Quest_SA, which uses the sentiment analysis technique to identify students’ emotions based on the answer provided by a closed-ended questionnaire. The polarity value is assigned for each questionnaire scale. The students’ responses are then gathered using a closed-ended questionnaire, and the student’s emotions are classified using a polarity-based method of sentiment analysis. Finally, sentiment scores and emotion variance were used to evaluate the outcomes. According to the sentiment ratings, students have favourable sentiments and emotions such as unhappy, somewhat happy, and happy. The real-world closed-ended questionnaires such as emotional intelligence, Eysenck, personality, self-determination scale, self-efficacy, Rosenberg’s self-esteem, positive and negative affect schedule, and Oxford happiness questionnaires were used to examine the academic performance with the proposed sentiment analysis. This study inferred that the proposed sentiment analysis preprocessing method with polarity scores is as accurate as the standard value calculation.