Analyzing Sentiments towards Performance of New Academic Programs in A Ghanaian University: A Case of Ghana Communication Technology University

Analyzing Sentiments towards Performance of New Academic Programs in A Ghanaian University: A Case of Ghana Communication Technology University | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing Sentiments towards Performance of New Academic Programs in A Ghanaian University: A Case of Ghana Communication Technology University Wonder Kwaku Susuassey, Daniel Adjei, Deborah Esenam Agbeli, Emmanuel Frimpong Asante, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6037147/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction of new programs in educational institutions in Ghana requires the introduction of a new curriculum. However, the performance of the new programs after the admission of students into the programs becomes questionable as the least performance is achieved. As Ghanaian univer- sities continue to expand their curriculum to meet the evolving demands of the digital economy, understanding the perceptions and experiences of the academic community becomes essential for gauging the success and potential areas for improvement of these programs. The study performs a supervised sentiment analysis on reviews gathered from students enrolled in new academic pro- grams towards the recommendation of the programs to people. The primary data for this study were drawn from a sample of 226 students, using both open-ended and closed-ended structured questionnaires. In the study, four machine learning methods, including Multinomial Naı̈ve Bayes, Support Vector Machine, Logistic Regression, and Random Forest were applied. The methods are compared, and the empirical findings reveal that the Support Vector Machine and Random For- est approaches outperformed the Multinomial Naı̈ve Bayes and Logistic Regression approaches, resulting in generally positive sentiment towards the performance of the new academic programs. By analyzing the sentiments of students, the study recommends actionable insights that can guide the university in enhancing its academic offerings and aligning them with the expectations of its community. Artificial Intelligence and Machine Learning Students Evaluation Quality of Education Sentiment Analysis Machine Learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6037147","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416295516,"identity":"36ba71a9-f3a3-488f-acbc-4c4cae836d79","order_by":0,"name":"Wonder Kwaku 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