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

Celena Chen, Celine Park, Jason Dwyer, Julie Medero


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.
Anthology ID:
S19-2164
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:
957–961
Language:
URL:
https://aclanthology.org/S19-2164
DOI:
10.18653/v1/S19-2164
Bibkey:
Cite (ACL):
Celena Chen, Celine Park, Jason Dwyer, and Julie Medero. 2019. Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 957–961, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector (Chen et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2164.pdf