CP-IMOS: A Cross-Platform Imbalance-Aware Methodology for Sentiment Classification of MOOC Reviews in IT Education
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MOOC review datasets offer a practical way to study course strengths, learner frustrations, and platform-level issues. However, sentiment models built from these data can give a distorted picture when positive ratings dominate the corpus. This paper presents CP-IMOS, a Cross-Platform Imbalance-Aware MOOC Sentiment Classification methodology, using public review data from Coursera, Udemy, and Stepik. The Coursera file contained 1,454,711 review rows; after cleaning and exact duplicate removal, 498,401 review records remained. Keyword filtering of course identifiers and titles then yielded 258,828 IT-related Coursera reviews from 215 course identifiers. For Udemy, a 200,000-comment Kaggle sample was processed with Course_info.csv to support second-platform validation. After cleaning, 46,377 comments from Development and IT & Software courses were retained from 15,836 course identifiers. The Stepik Russian-language corpus was included to test multilingual ingestion, duplicate removal, and Cyrillic-character validation during retrieval, but it was excluded from supervised sentiment training because it did not provide rating or sentiment labels. CP-IMOS maps rating-derived labels into five classes that correspond to ratings 1 through 5 and adds the Platform Sentiment Imbalance Index (PSII) to measure majority-class dominance before model interpretation. The classifier stage uses TF-IDF features with transparent machine-learning components. In the Coursera analysis sample, SGD Logistic Regression obtained the highest macro-F1 (0.383), while Multinomial Naive Bayes produced higher accuracy (0.759) but a weaker macro-F1 (0.250). In the Udemy validation set, SGD Logistic Regression also produced the highest macro-F1 (0.430). The findings show that cross-platform MOOC review data are strongly skewed toward positive ratings, although Udemy contains more non-positive ratings than Coursera. The main contribution is a reproducible retrieval-to-classification workflow that uses PSII, macro-F1, per-class metrics, and platform-specific interpretation before sentiment results are used in IT education analytics.
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Muhammad, I. (n.d.). Course Reviews on Coursera [Dataset]. Kaggle. Retrieved June 11, 2026, from https://www.kaggle.com/datasets/imuhammad/course-reviews-on-coursera
Hossain, G. H. (n.d.). Udemy Courses [Dataset]. Kaggle. Retrieved June 11, 2026, from https://www.kaggle.com/datasets/hossaingh/udemy-courses
Dyulicheva, Y. (2022). Dataset of MOOCs Reviews from Stepik on Russian Language [Dataset]. Mendeley Data, Version 1. https://doi.org/10.17632/8rwpvrw4hw.1
Chen, X., Wang, F., Cheng, G., Chow, M.-K., & Xie, H. (2022). Understanding learners' perception of MOOCs based on review data analysis using deep learning and sentiment analysis. Future Internet, 14(8), 218. https://doi.org/10.3390/fi14080218
Gomez, M. J., Calderón, M., Sánchez, V., Clemente, F. J. G., & Ruipérez-Valiente, J. (2022). Large scale analysis of open MOOC reviews to support learners' course selection. Expert Systems with Applications, 204, 118400. https://doi.org/10.1016/j.eswa.2022.118400
Jatain, D., Singh, V., & Dahiya, N. (2023). An exploration of the causal factors making an online course content popular and engaging. International Journal of Information Management: Data Insights, 3(2), 100194. https://doi.org/10.1016/j.jjimei.2023.100194
Chen, X., Zou, D., Xie, H., Cheng, G., Li, Z., & Wang, F. L. (2025). Automatic classification of online learner reviews via fine-tuned BERTs. International Review of Research in Open and Distributed Learning, 26(1). https://doi.org/10.19173/irrodl.v26i1.8068
Chen, X., Xie, H., Zou, D., Cheng, G., Tao, X., & Wang, F. (2025). Perceived MOOC satisfaction: A review mining approach using machine learning and fine-tuned BERTs. Computers and Education: Artificial Intelligence, 8, 100366. https://doi.org/10.1016/j.caeai.2025.100366
Dissanayake, A. D. M. B. B., & Fernando, P. (2025). Aspect-based sentiment analysis of student reviews on MOOC platforms. In 2025 5th International Conference on Advanced Research in Computing (ICARC). IEEE, Piscataway, NJ, USA. https://doi.org/10.1109/ICARC64760.2025.10963217
Awadh, W. A., Sulaiman, R. B., & Mahmoud, M. A. (2025). Aspect-based sentiment analysis in MOOCs: A systematic literature review introducing the MASC-MEF framework. Journal of King Saud University Computer and Information Sciences, 37, Article 2. https://doi.org/10.1007/s44443-025-00018-1
Aryal, S. K., Prioleau, H., Washington, G. J., & Burge, L. (2023). Evaluating ensembled transformers for multilingual code-switched sentiment analysis. In 2023 International Conference on Computational Science and Computational Intelligence. IEEE, Piscataway, NJ, USA. https://doi.org/10.1109/CSCI62032.2023.00032
Frias, R. R., Medina, R. P., & Sison, A. S. (2023). Attention-based bilateral LSTM-CNN for the sentiment analysis of code-mixed Filipino-English social media texts. In 2023 International Conference on Digital Applications, Transformation & Economy. IEEE, Piscataway, NJ, USA. https://doi.org/10.1109/ICDATE58146.2023.10248926
Kananta, G. S. C., Saputro, D. R. S., & Sutanto, S. (2026). Analysis of multilingual opinion polarization with cross-lingual language model-robustly optimized bidirectional encoder representations from transformers approach (XLM-RoBERTa). BAREKENG: Jurnal Ilmu Matematika dan Terapan, 20(2), 1709-1718. https://doi.org/10.30598/barekengvol20iss2pp1709-1718
Opitz, J. (2024). A closer look at classification evaluation metrics and a critical reflection of common evaluation practice. Transactions of the Association for Computational Linguistics, 12, 820–836. https://doi.org/10.1162/tacl_a_00675
Sujon, K. M., Hassan, R., Choi, K., & Samad, M. A. (2025). Accuracy, precision, recall, F1-score, or MCC? Empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models. Journal of Big Data, 12(1), Article 268. https://doi.org/10.1186/s40537-025-01313-4
Pant, H. V., Lohani, M. C., & Pande, J. (2023). Thematic and sentiment analysis of learners' feedback in MOOCs. Journal of Learning for Development, 10(1), 38–54. https://doi.org/10.56059/jl4d.v10i1.740
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2022). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003. https://doi.org/10.1016/j.nlp.2022.100003
Yan, C., Liu, J., Liu, W., & Liu, X. (2023). Sentiment analysis and topic mining using a novel deep attention-based parallel dual-channel model for online course reviews. Cognitive Computation, 15(1), 304–322. https://doi.org/10.1007/s12559-022-10083-7
Du, B. (2023). Research on the factors influencing the learner satisfaction of MOOCs. Education and Information Technologies, 28(2), 1935–1955. https://doi.org/10.1007/s10639-022-11269-0
Koufakou, A. (2024). Deep learning for opinion mining and topic classification of course reviews. Education and Information Technologies, 29, 2973–2997. https://doi.org/10.1007/s10639-023-11736-2
Marfani, H., Hina, S., & Tabassum, H. (2022). Analysis of learners' sentiments on MOOC forums using natural language processing techniques. In 2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) (pp. 1-8). IEEE. https://doi.org/10.1109/ICONICS56716.2022.10100401
Pan, X., Hu, B., Zhou, Z., & Feng, X. (2023). Are students happier the more they learn? Research on the influence of course progress on academic emotion in online learning. Interactive Learning Environments, 31(10), 6869–6889. https://doi.org/10.1080/10494820.2022.2052110
Liu, Z., Kong, X., Chen, H., Liu, S., & Yang, Z. (2023). MOOC-BERT: Automatically identifying learner cognitive presence from MOOC discussion data. IEEE Transactions on Learning Technologies, 16(4), 528–542. https://doi.org/10.1109/TLT.2023.3240715
Qu, Y., Li, F., Li, L., Dou, X., & Wang, H. (2022). Can we predict student performance based on tabular and textual data? IEEE Access, 10, 86008–86019. https://doi.org/10.1109/ACCESS.2022.3198682
Su, B., & Peng, J. (2023). Sentiment analysis of comment texts on online courses based on hierarchical attention mechanism. Applied Sciences, 13(7), 4204. https://doi.org/10.3390/app13074204
Prasad, S. B. A., & Nakka, R. P. K. (2023). Supervised sentiment analysis of indirect qualitative student feedback for unbiased opinion mining. Engineering Proceedings, 59(1), 15. https://doi.org/10.3390/engproc2023059015
Alzaid, M., & Fkih, F. (2023). Sentiment analysis of students' feedback on e-learning using a hybrid fuzzy model. Applied Sciences, 13(23), 12956. https://doi.org/10.3390/app132312956
Mabokela, K. R., Celik, T., & Raborife, M. (2023). Multilingual sentiment analysis for under-resourced languages: A systematic review of the landscape. IEEE Access, 11, 15996–16020. https://doi.org/10.1109/ACCESS.2022.3224136
Ganganwar, V., Rajalakshmi, R., & Gayathri, S. (2024). Employing synthetic data for addressing the class imbalance in aspect-based sentiment classification. Journal of Information and Telecommunication, 8(2), 167–188. https://doi.org/10.1080/24751839.2023.2270824
Suhaeni, C., Yong, H. S., & Ryu, H. (2024). Enhancing imbalanced sentiment analysis: A GPT-3-based sentence-by-sentence generation approach. Applied Sciences, 14(2), 622. https://doi.org/10.3390/app14020622
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