Literature permeates through 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, debate over, or enlighten ourselves with. However, the existence of subtle themes that are difficult to discern had inspired us to utilize four machine learning algorithms: Decision Trees, Random Forest, Logistic Regression, and Support Vec- tor Classifier (SVC) to aid in their detection. Trained on the ValueEval data set as a multi- label classification problem, the supervised ma- chine learning models did not perform as well as expected, with F1 metrics hovering from 0.0 to 0.04 for each value. Noting this, the lim- itations and weaknesses of our approach are discussed in our paper.