We use the 600 hand-labelled articles from SemEval Task 4 (Kiesel et al.. 2019) 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.
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机译：我们使用SemEval Task 4（Kiesel et al..2019）中的600篇带有手工标记的文章来为Hyperpartisan News Detection任务手动调整具有3000个功能的分类器。我们的最终系统使用基于词袋（BoW）的功能，文章标题的分析，语言的复杂性以及朴素的贝叶斯分类器中的简单情感分析。我们根据发布商标记的60万篇文章对最终系统进行了培训。我们最终的系统在手工标记的测试集上的精度为0.653。最有效的功能是自动可读性索引和标题中包含某些单词。这表明超党派写作使用了独特的写作风格，尤其是标题。
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