Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Helen Josephine, V.L.; Gupta, Sharad; Joy, Rosewine; Sharma, Manjari Title A name given to the resource Enhancing Online Education Through Sentiment Analysis and Complex Systems Modelling Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Northeast Journal of Complex Systems;Volume;7;Issue;1;Article No.;11; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.63562/2577-8439.1094" target="_blank" rel="noreferrer noopener">https://doi.org/10.63562/2577-8439.1094</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005998452?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005998452?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Helen Josephine V.L., Business Analytics, School of Business and Management, Christ University, Bangalore, India; Gupta S., Christ University, Bangalore, India; Joy R., Business Analytics, School of Business and Management, Christ University, Bangalore, India; Sharma M., Christ University, Bangalore, India Description An account of the resource This research explores the application of sentiment analysis through the lens of complex systems modelling to enhance the quality of online certification courses, with a particular focus on global platforms such as Coursera. The COVID-19 pandemic catalyzed significant growth in online learning, creating an urgent need for adaptive and student-centric approaches to ensure relevance and effectiveness. Leveraging unstructured textual data from student reviews of courses, this study integrates methodologies from systems science, computer science, and education to address real-world challenges in online education. By employing both lexiconbased (SentiWordNet and VADER) and supervised machine learning techniques (Multinomial Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent), the research conducts a detailed sentiment analysis to identify patterns, emergent behaviours, and feedback loops inherent in course design and delivery. Findings reveal that Support Vector Machine achieves the highest accuracy at 97.3%, offering insights that guide iterative improvements in course content and pedagogical strategies. The study demonstrates how interdisciplinary approaches to sentiment analysis can inform responsive education environments, aligning with broader societal goals of accessibility, inclusivity, and quality in online learning ecosystems. 2025, Binghamton University Libraries. All rights reserved. Subject The topic of the resource Complex Systems Modelling; Machine Learning Techniques; Online Certification Courses; Sentiment Analysis; Student-Centric Education Publisher An entity responsible for making the resource available Binghamton University Libraries Relation A related resource ISSN: 25778439; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online