Abstract

A knowledgeable and skilled populace is fostered by reforming the educational system, which is a pillar of societal growth. However, in order to guide reform initiatives, it is crucial to comprehend how the general population feels and perceives the current educational system. Using machine learning methods and natural language processing techniques, this study uses sentiment analysis to determine how the general public feels about the educational system. It offers a methodology for using sentiment analysis to collect and examine public opinion on a range of topics related to the educational system, such as infrastructure, legislation, teaching strategies, and curriculum design. In order to find dominant sentiments and trends, the process entails gathering textual data from a variety of sources, including surveys, online forums, and social media platforms, and then using sentiment analysis algorithms. Key areas of concern, including as infrastructure, teacher quality, and curriculum relevance, are revealed by our examination of internet forums, social media, and survey responses. In order to improve the educational system's quality, relevance, and efficacy in fulfilling the demands of future generations, policymakers, educators, and other stakeholders can benefit greatly from the findings. This information can be utilized to pinpoint problem areas, rank reforms, and put evidence-based tactics into practice to raise the standard and applicability of education. This study intends to support a more adaptable and successful educational system that is in line with the changing requirements and goals of the general public by utilizing sentiment analysis.

References

• Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R. 2011. Sentiment analysis of Twitter data. In Proceedings of the Workshop on Languages in Social Media, 30–38. https://www.aclweb.org/anthology/W11-2104
• Alhojailan, M. I. 2012. Thematic analysis: A critical review of its process and evaluation. West East Journal of Social Sciences, 1(1), 8–21.
• Bowen, G. A. 2009. Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
• Government of India. 1968. National Education Policy, 1968. Retrieved from https://web.archive.org/web/20090731002808/http://www.education.nic.in/policy/npe-1968.pdf
• Holloway, I., and Galvin, K. 2016. Qualitative Research in Nursing and Healthcare. John Wiley & Sons. IGNOU. 1985. The Indira Gandhi National Open University Act 1985 (No. 50 of 1985). Retrieved from http://www.ignou.ac.in/userfiles/IGNOU_ACT(Amended%20till%2024_09_19).pdf Kaurav, R. P. S., Rajput, S., and Baber, R. 2019. Factors affecting the acceptance of e-learning by students: A study of e-learning programs in Gwalior, India. South Asian Journal of Management, 26(1), 76–95.
• Pang, B., and Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://www.nowpublishers.com/article/Details/INR-011
• Liu, B. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016
• Rizvi, F., and Lingard, B. 2009. Globalising Education Policy. Routledge.
• Silova, I., Rappleye, J., and Auld, E. 2020. Beyond the western horizon: Rethinking education, values, and policy transfer. In Handbook of Education Policy Studies, 3–29. Springer, Singapore.
• Thelwall, M., Buckley, K., and Paltoglou, G. 2012. Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163–173.
• Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558. https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.21416