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Authors:
Costin-Gabriel Chiru
Keywords: Creativity; Satire; Natural Language Processing; Metrics for Creativity Detection
Abstract:
In this paper, we present a model that was intended to discriminate creative from non-creative news articles. In order to build the classifier, we have combined nine different measures using a stepwise logistic regression model. The obtained model was tested in two experiments: the first one tried to discriminate between news articles about the US 2012 Elections from different newspapers versus articles taken from The Onion (a website providing satiric news) on the same subject, while the second one evaluated the capacity of the model to generalize over different topics and text genres. The experiments showed that the system achieves 80% accuracy, but the lack of true positives from the second experiment raised the question of whether we really identified creativity or in fact we detected satire (as the assumption for the training corpus was that the satiric news from The Onion were also creative).
Pages: 174 to 180
Copyright: Copyright (c) IARIA, 2013
Publication date: June 23, 2013
Published in: conference
ISSN: 2308-3972
ISBN: 978-1-61208-280-6
Location: Rome, Italy
Dates: from June 23, 2013 to June 28, 2013