Team Ned Leeds at SemEval-2019 Task 4: Exploring Language Indicators of Hyperpartisan Reporting
Date Issued
2019-06
Author(s)
Stevanoski, Bozhidar
Abstract
This paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The results of the evaluation of different feature sets and the extent
to which various feature categories could affect the performance metrics are discussed and
compared. Among different combinations of features and classifiers, Random Forest classifier using vector representations of the headline and the text of the report, with the inclusion of 8 readability scores and few stylistic features yielded best result, ranking our team at the 9 th place at the SemEval 2019 Hyperpartisan News Detection challenge.
to which various feature categories could affect the performance metrics are discussed and
compared. Among different combinations of features and classifiers, Random Forest classifier using vector representations of the headline and the text of the report, with the inclusion of 8 readability scores and few stylistic features yielded best result, ranking our team at the 9 th place at the SemEval 2019 Hyperpartisan News Detection challenge.
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