Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector
Evan Amason, Jake Palanker, Mary Clare Shen, Julie Medero · 2019
We use the 600 hand-labelled articles from Se-mEval Task 4 (Kiesel et al., 2019) to handtune 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.