Friedrich Nietzsche at SemEval-2023 Task 4: Detection of Human Values from Text Using Machine Learning

Abstract

Literature permeates almost every facet of our lives, whether through books, magazines, or internet articles. Moreover, every piece of written work contains ideas and opinions that we tend to relate to, accept or disregard, the debate over, or enlighten ourselves. However, the existence of subtle themes that are difficult to discern inspired us to utilize four machine learning algorithms: Decision Trees, Random Forest, Logistic Regression, and Support Vector Classifier (SVC) to aid in their detection. Trained on the ValueEval data set as a multi-label classification problem, the supervised machine learning models did not perform as well as expected, with F1 metrics hovering from 0.0 to 0.04 for each value. Noting this, our paper discusses our approach’s limitations and weaknesses.