近年來，大規模開放式線上課程（Massive Online Open Courses, MOOCs）已成為全球教育的重要趨勢。這種開放、無地域限制的教學形式吸引了數百萬使用者，提供了多元課程選擇和便利的學習機會。疫情期間，對遠距教學的需求劇增，MOOCs也更廣泛地受到關注。近年來，情感分析方法逐漸成為學者關注的焦點，能快速地獲取大量使用者評論資料，以即時了解使用者對課程的看法。然而，在MOOCs研究中的應用中，粗粒度情感分析方法僅僅提供了文本的整體情感極性，無法捕捉到文本中的特定面向或細節，而失去一些重要資訊，限制了對學習體驗和回饋的理解。因此，本研究使用細粒度的基於面向之情感分析方法作為研究方法。
本研究旨在分析管理類課程的使用者評論。本研究收集了來自多個領域的管理類課程的使用者評論資料，透過(1)萃取使用者評論中提及的產品面向；(2)識別每個評論中的面向並判斷每個面向為正面或負面情感極性；(3)總結結果，對這些資料進行了基於面向之情感分析，並針對不同領域之管理類課程，探討各領域之間的差異性與共通性，並針對提及次數較多之面向，找出與之相似或關聯性較高的詞彙。
研究發現，使用者對管理類MOOCs課程的內容大多給予正面評價，且使用者對於課程內容、課程任務、教材等面向提及頻率較高。另外，在不同領域管理類課程的學生評論中，某些面向的提及頻率有顯著差異，但也存在一些共同的關注要點，如課程內容、教師及教材。此外，本研究也對於提及頻率較高之面向進行分析，從而更好地理解和分析文本內容，提高情感分析的準確性。

In recent years, Massive Open Online Courses (MOOCs) have become an important trend in global education field. On the other hand, sentiment analysis methods have gradually become the focus of scholars' attention, which can quickly obtain a large amount of user review data to instantly understand users' opinions in the courses. However, in applications in MOOCs research, coarse-grained sentiment analysis methods only provide the overall sentiment polarity of the text and are unable to capture specific aspects or details in the text, thus losing some important information and feedback. Therefore, this study uses aspect-based sentiment analysis as a research method.
This study collected user review data from management courses in multiple fields, by (1) extracting the product aspects mentioned in user reviews; (2) identifying the aspects in each review and judging each aspect as positive or negative polarity; (3) Summarize the results, explore the differences and commonalities between management courses in different fields and find words that are similar or highly related.
The study found that users mostly gave positive comments in the content of management MOOCs courses, and users mentioned the course content, assignment, teaching materials and other aspects more frequently. In addition, in the comments of students on management courses in different fields, there are significant differences in the frequency of mentioning certain aspects, but there are also some common points, such as course content, teachers, and teaching materials.

Abdelghani, B. (2020). Trends in MOOCs research: Analysis of educational technology journals. International Journal of Emerging Technologies in Learning (iJET), 15(17), 47–68.
Almatrafi, O., Johri, A., & Rangwala, H. (2018). Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums. Computers & Education, 118, 1-9.
Almatrafi, O., & Johri, A. (2022). Improving MOOCs Using Information From Discussion Forums: An Opinion Summarization and Suggestion Mining Approach. IEEE Access, 10, 15565-15573.
Badali, M., Hatami, J., Banihashem, S. K., Rahimi, E., Noroozi, O., & Eslami, Z. (2022). The role of motivation in MOOCs’ retention rates: a systematic literature review. Research and Practice in Technology Enhanced Learning, 17(1), 5.
Bastos, M. T., & Mercea, D. (2019). The Brexit botnet and user-generated hyperpartisan news. Social Science Computer Review, 37(1), 38-54.
Billsberry, J., & Alony, I. (2023). The MOOC Post-Mortem: Bibliometric and Systematic Analyses of Research on Massive Open Online Courses (MOOCs), 2009 to 2022. Journal of Management Education.
Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
Chanaa, A., & El Faddouli, N. (2022). Sentiment analysis on Massive Open Online Courses (MOOCs): Multi-factor analysis, and machine learning approach. International Journal of Information and Communication Technology Education (IJICTE), 18(1), 1–22.
Chauhan, G. S., Agrawal, P., & Meena, Y. K. (2019). Aspect-based sentiment analysis of students’ feedback to improve teaching–learning process. In Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 2 (pp. 259-266). Springer Singapore.
Chehal, D., Gupta, P., & Gulati, P. (2021). Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis. Journal of Web Engineering, 21(2), 145–178.
Colicev, A., Kumar, A., & O'Connor, P. (2019). Modeling the relationship between firm and user generated content and the stages of the marketing funnel. International Journal of Research in Marketing, 36(1), 100-116.
Cox, C., Burgess, S., Sellitto, C., & Buultjens, J. (2009). The role of user-generated content in tourists’travel planning behavior. Journal of Hospitality Marketing & Management, 18(8), 743–764.
Dalipi, F., Zdravkova, K., & Ahlgren, F. (2021). Sentiment analysis of students’ feedback in MOOCs: A systematic literature review. Frontiers in Artificial Intelligence, 4, 728708.
Das, N., Gupta, S., Das, S., Yadav, S., Subramanian, T., & Sarkar, N. (2021). A Comparative Study of Sentiment Analysis Tools. In 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-7).
Dillahunt, T., Wang, Z., & Teasley, S. D. (2014). Democratizing higher education: Exploring MOOC use among those who cannot afford a formal education. International Review of Research in Open and Distributed Learning, 15(5), 177-196.
Edalati, M., Imran, A. S., Kastrati, Z., & Daudpota, S. M. (2022). The Potential of Machine Learning Algorithms for Sentiment Classification of Students’ Feedback on MOOC. In Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 3 (pp. 11-22). Springer International Publishing.
Egloffstein, M., Koegler, K., & Ifenthaler, D. (2019). Instructional quality of business MOOCs: Indicators and initial findings. Online Learning, 23(4), 85-105. Farias, D. H., & Rosso, P. (2017). Irony, sarcasm, and sentiment analysis. In Sentiment analysis in social networks (pp. 113-128). Morgan Kaufmann.
Gavali, P. M., & Shiragave, S. K. (2023). Text representation for sentiment analysis: From static to dynamic. In 2023 3rd International Conference on Smart Data Intelligence (ICSMDI) (pp. 99-105).
Geng, R., & Chen, J. (2021). The Influencing Mechanism of Interaction Quality of UGC on Consumers’ Purchase Intention – An Empirical Analysis. Frontiers in Psychology, 12, 697382.
Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos. In Proceedings of the First ACM Conference on Learning @ Scale Conference (pp. 41–50).
Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724.
He, P., Liu, X., Gao, J., & Chen, W. (2021). DeBERTa: Decoding-enhanced BERT with Disentangled Attention. arXiv preprint arXiv:2006.03654.
He, P., Gao, J., & Chen, W. (2023). DeBERTaV3: Improving DeBERTa using ELECTRAStyle Pre-Training with Gradient-Disentangled Embedding Sharing. arXiv preprint arXiv:2111.09543.
Hood, N., & Littlejohn, A. (2016). MOOC quality: The need for new measures. Journal of Learning for Development, 3(3).
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp.168–177).
Huang, H., Jew, L., & Qi, D. (2023). Take a MOOC and then drop: A systematic review of MOOC engagement pattern and dropout factor. Heliyon, 9(4), e15220.
Karsen, M., Uzir, N. A., Abdul Rahman, S. B., & Kurniawan, Y. (2023). Incompletion rate factors in MOOC: A systematic literature review. In 2023 International Conference on University Teaching and Learning (InCULT) (pp. 1-6). Shah Alam, Malaysia.
Lei, J., & Zhang, Q. (2023). What matters to LMOOC learners: Content and sentiment analyses of learner course reviews. Computer Assisted Language Learning, 1–27.
Li, X., Bing, L., Zhang, W., & Lam, W. (2019). Exploiting BERT for end-to-end aspectbased sentiment analysis. arXiv preprint arXiv:1910.00883.
Li, L., Johnson, J., Aarhus, W., & Shah, D. (2022). Key factors in MOOC pedagogy based on NLP sentiment analysis of learner reviews: What makes a hit. Computers & Education, 176, 104354.
Lin, Y., Wang, C., Song, H., & Li, Y. (2021). Multi-Head Self-Attention Transformation Networks for Aspect-Based Sentiment Analysis. IEEE Access, 9, 8762-8770.
Lundqvist, K., Liyanagunawardena, T., & Starkey, L. (2020). Evaluation of student feedback within a MOOC using sentiment analysis and target groups. The International Review of Research in Open and Distributed Learning, 21(3), 140-156.
Malthouse, E. C., Calder, B. J., Kim, S. J., & Vandenbosch, M. (2016). Evidence that user-generated content that produces engagement increases purchase behaviours. Journal of Marketing Management, 32(5–6), 427–444.
Meek, S. E. M., Blakemore, L., & Marks, L. (2016). Is peer review an appropriate form of assessment in a MOOC? Student participation and performance in formative peer review. Assessment & Evaluation in Higher Education, 42(6), 1000–1013.
Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J., Estévez-Ayres, I., & Kloos, C. D. (2018). Sentiment analysis in MOOCs: A case study. In 2018 IEEE global engineering education conference (EDUCON) (pp. 1489-1496).
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) (pp. 79-86), Philadelphia, Pennsylvania, USA.
Rabin, E., Kalman, Y. M., & Kalz, M. (2019). An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. International Journal of Educational Technology in Higher Education, 16(1), 1-20.
Rachid, B. A., Azza, H., & Henda, B. G. (2018). Sentiment analysis approaches based on granularity levels. In Proceedings of the 14th International Conference on Web Information Systems and Technologies (Vol. 1, pp. 324-331).
Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623-2656.
Rekha, I. S., Shetty, J., & Basri, S. (2023). Students’ continuance intention to use MOOCs: empirical evidence from India. Education and Information Technologies, 28(4), 4265-4286.
Saura, J.R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2023). Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks. Information Systems and e-Business Management.
Shah, V., Murthy, S., Warriem, J., Sahasrabudhe, S., Banerjee, G., & Iyer, S. (2022). Learner-centric MOOC model: A pedagogical design model towards active learner participation and higher completion rates. Educational technology research and development, 70(1), 263-288.
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003.
Shaukat, Z., Zulfiqar, A. A., Xiao, C., Azeem, M. M., Mahmood, T. (2020). Sentiment analysis on IMDB using lexicon and neural networks. SN Applied Sciences, 2, 148.
Shayaa, S., Jaafar, N. I., Bahri, S., Sulaiman, A., Wai, P. S., Chung, Y. W., ... & Al-Garadi, M. A. (2018). Sentiment analysis of big data: methods, applications, and open challenges. IEEE Access, 6, 37807-37827.
Staubitz, T., Petrick, D., Bauer, M., Renz, J., & Meinel, C. (2016). Improving the peer assessment experience on MOOC platforms. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (pp. 389-398).
Turney, P. D. (2002). Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics(pp. 417-424), Philadelphia, Pennsylvania, USA.
Vassilikopoulou, A., Kamenidou, I., & Priporas, C. V. (2024). Negative Airbnb reviews: an aspect-based sentiment analysis approach. EuroMed Journal of Business, 19(2), 191-207.
Xiang, Y., Zhang, J., Zhang, Z., Yu, Z., & Xian, Y. (2022). Aspect-Based Sentiment Analysis with Position Embedding Interactive Attention Network. Journal of Information Processing Systems, 18(5), 614-627.
Xiao, Y., Qi, C., & Leng, H. (2021). Sentiment analysis of Amazon product reviews based on NLP. In 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) (pp. 1218-1221). Changsha, China.
Yang, H., Zeng, B., Yang, J., Song, Y., & Xu, R. (2021). A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction. Neurocomputing, 419, 344-356.
Zankadi, H., Idrissi, A., Daoudi, N., & Hilal, I. (2023). Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques. Education and Information Technologies, 28(5), 5567-5584.
Zhang, L., Wang, S., & Liu, B. (2018). Deep Learning for Sentiment Analysis: A Survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
Zhang, D., & Sheng, D. (2021). EFL Lecturers’ Metadiscourse in Chinese University MOOCs Across Course Types. Corpus Pragmatics, 5(2), 243–270.
Zhu, M., Sari, A. R., & Lee, M. M. (2020). A comprehensive systematic review of MOOC research: Research techniques, topics, and trends from 2009 to 2019. Educational Technology Research and Development, 68, 1685-1710.