Leveraging Machine Learning Sentiment Analysis to Assess Learning Impact

Abstract

In the rapidly evolving landscape of education and professional development, the evaluation of learning impact has become increasingly crucial. Traditional methods of assessment often fall short in capturing the nuanced and dynamic nature of learning outcomes. This paper explores the application of machine learning sentiment analysis as a novel and effective approach to evaluate the impact of learning experiences. By harnessing the power of natural language processing and data analytics, we aim to provide educators and institutions with a robust framework for assessing the emotional and cognitive impact of their educational programs. This paper discusses the theoretical foundations, methodology, and potential benefits of utilizing sentiment analysis in learning impact evaluation.