Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector

Evan Amason; Jake Palanker; Mary Clare Shen; Julie Medero

doi:10.18653/v1/S19-2166

Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector

Evan Amason, Jake Palanker, Mary Clare Shen, Julie Medero

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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.

Anthology ID:: S19-2166
Volume:: Proceedings of the 13th International Workshop on Semantic Evaluation
Month:: June
Year:: 2019
Address:: Minneapolis, Minnesota, USA
Editors:: Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 967–970
Language:
URL:: https://aclanthology.org/S19-2166/
DOI:: 10.18653/v1/S19-2166
Bibkey:
Cite (ACL):: Evan Amason, Jake Palanker, Mary Clare Shen, and Julie Medero. 2019. Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 967–970, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):: Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector (Amason et al., SemEval 2019)
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PDF:: https://aclanthology.org/S19-2166.pdf

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@inproceedings{amason-etal-2019-harvey,
    title = "Harvey Mudd College at {S}em{E}val-2019 Task 4: The {D}.{X}. Beaumont Hyperpartisan News Detector",
    author = "Amason, Evan  and
      Palanker, Jake  and
      Shen, Mary Clare  and
      Medero, Julie",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2166/",
    doi = "10.18653/v1/S19-2166",
    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."
}

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%0 Conference Proceedings
%T Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector
%A Amason, Evan
%A Palanker, Jake
%A Shen, Mary Clare
%A Medero, Julie
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F amason-etal-2019-harvey
%X 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.
%R 10.18653/v1/S19-2166
%U https://aclanthology.org/S19-2166/
%U https://doi.org/10.18653/v1/S19-2166
%P 967-970

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Markdown (Informal)

[Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector](https://aclanthology.org/S19-2166/) (Amason et al., SemEval 2019)

Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector (Amason et al., SemEval 2019)

ACL

Evan Amason, Jake Palanker, Mary Clare Shen, and Julie Medero. 2019. Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 967–970, Minneapolis, Minnesota, USA. Association for Computational Linguistics.