Comparison of Sentiment Analysis Methods Used to Investigate the Quality of Teaching Aids Based on Virtual Simulators of Embedded Systems

Abstract

Virtual simulators of embedded systems and analyses of student surveys regarding their use at the early stage of the process of learning embedded systems, are presented in this article. The questionnaires were prepared in the Polish language, and the answers were automatically translated into English using two publicly available translators. The results of users’ experiences and feelings related to the use of virtual simulators are shown on the basis of detected sentiment using three chosen analysis methods: the Flair NLP library, the Pattern library, and the BERT NLP model. The results of the selected sentiment detection methods were compared and related to users reference answers, which gives information about the methods quality of the methods and their possible use in the automated review analysis process. This paper comprises detailed sentiment analysis results with a broader statistical approach for each question. Based on the students feedback and sentiment analysis, a new version of the TMSLAB v.2 virtual simulator was created.