@InProceedings{amason-EtAl:2019:S19-2,
  author    = {Amason, Evan  and  Palanker, Jake  and  Shen, Mary Clare  and  Medero, Julie},
  title     = {Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {967--970},
  abstract  = {We use the 600 hand-labelled articles from SemEval Task 4 to hand-tune a classifier with 3000 features for the Hyperpartisan News Detection task. Our final system uses features based on bag-of-words (BoW), analysis of the article title, language complexity, and simple sentiment analysis in a naive Bayes classifier. We trained our final system on the 600,000 articles labelled by publisher. Our final system has an accuracy of 0.653 on the hand-labeled test set. The most effective features are the Automated Readability Index and the presence of certain words in the title. This suggests that hyperpartisan writing uses a distinct writing style, especially in the title.},
  url       = {http://www.aclweb.org/anthology/S19-2166}
}