College Course Recommender System based on Sentiment Analysis

Abstract

College plays a vital role in defining a student's future by providing relevant education, skills, and exposure. The choice of college courses heavily influences their career foundation and employment skill sets. However, the expanding number of college courses often leaves students struggling to make the best choice, leading to dropouts due to the lack of interest. Many systems rely on existing student reviews or the popularity of the course itself, which may not always result in relevant recommendations. Hence, some systems use sentiment analysis (SA) to evaluate students' opinions, considering qualitative and sentiment data to understand their interests better. However, current SA performance struggles to extract meaningful words due to dataset availability. Hence, a course recommendation system based on students' interests and competence would be valuable. This paper focuses on evaluating and understanding existing systems to provide students with an effective course recommendation system. It includes first gathering useful data that would improve the use of SA. Next, feature extraction techniques Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram were implemented and compared. SA will be performed to increase the relevance of the student's interests to recommend a course by implementing Fuzzy Logic and K-nearest neighbors. These algorithms will be evaluated by performance metrics such as accuracy to determine the most efficient way to recommend a course. The findings highlight the importance of considering students' subjective preferences and interests for better outcomes regarding student success and graduation rates.