This document describes a machine learning approach to identify bias in news articles. It scrapes over 900 news and opinion articles, removes common words, and uses a multinomial Naive Bayes classifier to count word frequencies and calculate the probability that an article belongs to the news or opinion class. Cross-validation of this approach achieved over 90% accuracy in distinguishing news from opinion articles.