DOI : https://doi.org/10.18517/ijaseit.13.5.19032

College Course Recommender System based on Sentiment Analysis

Naufal Zamri (1), Naveen Palanichamy (2), Su-Cheng Haw (3)
(1) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(2) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(3) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
Zamri, Naufal, et al. “College Course Recommender System Based on Sentiment Analysis”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 5, Oct. 2023, pp. 1984-92, doi:10.18517/ijaseit.13.5.19032.
Citation Format :
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.

Ng, Yiu-Kai, Linn, & Jane. (2017). 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). In CrsRecs: A personalized course recommendation system for college students

Gomez, M. J., Calderón, M., Sí¡nchez, V., Garcí­a Clemente, F. J., & Ruipí©rez-Valiente, J. A. (2022). Large scale analysis of open MOOC reviews to support learners' course selection. Expert Systems with Applications.

Kumar, R., & Bhatia, S. (2021). Course Recommender System Architecture with Sentiment Score. Proceedings of the International Conference on Innovative Computing & Communication (ICICC) 2021.

Esteban, A., Zafra, A., & Romero, C. (2020). Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowledge-Based Systems.

Li, J., & Ye, Z. (2020). Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm. Complexity.

Morsomme, R., & Alferez, S. V. (2019). Content-based Course Recommender System for Liberal Arts Education. In Educational Data Mining.

Ezaldeen, H., Misra, R., Bisoy, S. K., Alatrash, R., & Priyadarshini, R. (2022). A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis. Journal of Web Semantics.

Gulzar, Z., Leema, A. A. (2018). Course recommendation based on query classification approach. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT),13(3), 69-83.

Vedavathi, N., & Anil Kumar, K.M. (2023). E-learning course recommendation based on sentiment analysis using hybrid Elman similarity. Knowledge-Based Systems.

Mawane, J., Naji, A., & Ramdani, M. (2020). Recommender E-Learning Platform Using Sentiment Analysis Aggregation. Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications.

Jiang, X., Bai, L., Yan, X., & Wang, Y. (2022). LDA-based online intelligent courses recommendation system. Evolutionary Intelligence, 1-7.

Hasan, Z., & Baskaran, S. S. (2023, March). Propose a Recommender System to Dynamically Align Higher Education Curriculums With 4IR Market Needs. In 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD) (pp. 1-7). IEEE.

Julianti, M. R., Heryadi, Y., Yulianto, B., & Budiharto, W. (2022, August). Recommendation System Model for Personalized Learning in Higher Education using Content-Based Filtering Method. In 2022 International Conference on Information Management and Technology (ICIMTech) (pp. 1-6). IEEE.

Muhammad Shafiq, Ng, H., Yap, T. T., & Goh, V. T. (2022). Performance of Sentiment Classifiers on Tweets of Different Clothing Brands. Journal of Informatics and Web Engineering.

Shahbazi, Z., & Byun, Y. C. (2022). Agent-based recommendation in E-learning environment using knowledge discovery and machine learning approaches. Mathematics, 10(7), 1192.

Vanitha, V., Krishnan, P., & Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in E-learning. Computers & Electrical Engineering.

Shan, V. L., & Beng, G. K. (2023). Cuffless Non-invasive Blood Pressure Measurement Using CNN-LSTM Model: A Correlation Study. International Journal on Robotics, Automation and Sciences.

Alatrash, R., Ezaldeen, H., Misra, R., & Priyadarshini, R. (2021). Sentiment Analysis Using Deep Learning for Recommendation in E-Learning Domain. In Progress in Advanced Computing and Intelligent Engineering.

Kumar, T., Sankaran, K. S., Ritonga, M., Asif, S., Kumar, C. S., Mohammad, S., ... & Asenso, E. (2022). Research Article Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students. Mathematical Problems in Engineering.

Ghosh, S. (2023, February). A Hybrid Programming Course recommendation system using Fuzzy Logic and xDeepFM. In 2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) (pp. 1-8). IEEE.

Stein, S. A., Weiss, M., Gary, Chen, Yiwen, & Leeds, D. D. (2020). A College Major Recommendation System. Proceedings of the 14th ACM Conference on Recommender Systems.

Cheng, J.-P., & Haw, S.-C. (2023). Mental Health Problems Prediction Using Machine Learning Techniques. International Journal on Robotics, Automation and Sciences.

Lim, S. T., Yuan, J. Y., Khaw, K. W., & Chew, X. (2023). PredictingTravel Insurance Purchases in an Insurance Firm through Machine Learning Methods afterCOVID-19. Journal of Informatics and Web Engineering.

Nagaraj, P., Saiteja, K., Ram, K. K., Kanta, K. M., Aditya, S. K., & Muneeswaran, V. (2022, April). University Recommender System based on Student Profile using Feature Weighted Algorithm and KNN. Proceedings of the International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 479-484). IEEE.

Jena, K. K., Bhoi, S. K., Malik, T. K., Sahoo, K. S., Jhanjhi, N. Z., Bhatia, S., & Amsaad, F. (2022). E-Learning Course Recommender System Using Collaborative Filtering Models. Electronics, 12(1), 157.

Fazazi, H. E., Qbadou, M., Salhi, I., & Mansouri, K. (2018). Personalized recommender system for e-Learning environment based on student's preferences. In IJCSNS International Journal of Computer Science and Network Security.

Tang, H., Xu, Y., Lin , A., Heidari, A. A., Wang, M., Chen, H., . . . Li, C. (2020). Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers. IEEE Access.

Odry, Akos and Tadic, Laslo, V., & Peter. (2021). A Stochastic Logic-Based Fuzzy Logic Controller: First Experimental Results of a Novel Architecture. IEEE Access.

Zaveri, A. A., Mashood, R., Shehmir, S., Parveen, M., Sami, N., & Nazar, M. (2023). AIRA: An Intelligent Recommendation Agent Application for Movies. Journal of Informatics and Web Engineering.

Razak, T. R., Garibaldi, M. J., Wagner, C., Pourabdollah, A., & Soria, D. (2021). Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach. IEEE Transactions on Fuzzy Systems.

Sharma, M., Nitesh, D., Vandana, & Mishra, V. (2021). Mediative fuzzy logic mathematical model: A contradictory management prediction in COVID-19 pandemic. Applied Soft Computing.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).