Ethical and Societal Implication of Sentiment Analysis using NLP in Educational Feedback System

Abstract

Educational feedback, particularly through reviews provided by students, serves as a cornerstone for advancing the academic and administrative functions of Higher Education Institutions (HEIs). The online reputation of an HEI is a critical intangible asset, and the inability to effectively address negative feedback on digital platforms can lead to significant reputational and operational repercussions. To promote continuous institutional improvement, it is imperative to systematically identify and interpret the sentiments expressed by stakeholders. This study focuses on analyzing stakeholder sentiments derived from text-based feedback to offer actionable insights that drive institutional enhancement. By employing opinion mining techniques, the research evaluates the applicability and effectiveness of current Natural Language Processing (NLP) methodologies in educational contexts. Furthermore, it examines emerging trends and challenges associated with NLP adoption in the education sector, alongside exploring the ethical and societal implications of implementing sentiment analysis within educational feedback systems.