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Classifying news versus opinions in newspapers: Linguistic features for domain independence

Published online by Cambridge University Press:  21 February 2017

K. R. KRÜGER ,
A. LUKOWIAK ,
J. SONNTAG ,
S. WARZECHA  and
M. STEDE
K. R. KRÜGER
Affiliation:
University of Potsdam, FSP Cognitive Science, Applied Computational Linguistics, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
A. LUKOWIAK
Affiliation:
University of Potsdam, FSP Cognitive Science, Applied Computational Linguistics, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
J. SONNTAG
Affiliation:
University of Potsdam, FSP Cognitive Science, Applied Computational Linguistics, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
S. WARZECHA
Affiliation:
University of Potsdam, FSP Cognitive Science, Applied Computational Linguistics, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
M. STEDE
Affiliation:
University of Potsdam, FSP Cognitive Science, Applied Computational Linguistics, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
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Abstract

Newspaper text can be broadly divided in the classes ‘opinion’ (editorials, commentary, letters to the editor) and ‘neutral’ (reports). We describe a classification system for performing this separation, which uses a set of linguistically motivated features. Working with various English newspaper corpora, we demonstrate that it significantly outperforms bag-of-lemma and PoS-tag models. We conclude that the linguistic features constitute the best method for achieving robustness against change of newspaper or domain.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 5 , September 2017 , pp. 687 - 707
Copyright
Copyright © Cambridge University Press 2017 

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