Harvey Mudd College at SemEval-2019 task 4: The Carl Kolchak Hyperpartisan News Detector

Abstract

We use various natural processing and machine learning methods to perform the Hyperpartisan News Detection task. In particular, some of the features we look at are bag-of-words features, the title's length, number of capitalized words in the title, and the sentiment of the sentences and the title. By adding these features, we see improvements in our evaluation metrics compared to the baseline values. We find that sentiment analysis helps improve our evaluation metrics. We do not see a benefit from feature selection. Overall, our system achieves an accuracy of 0.739, finishing 18th out of 42 submissions to the task. From our work, it is evident that both title features and sentiment of articles are meaningful to the hyperpartisanship of news articles.