Journal
KNOWLEDGE-BASED SYSTEMS
Volume 69, Issue -, Pages 124-133Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2014.05.021
Keywords
Emotional language; Social web; Feature extraction; Sarcasm; Nastiness
Categories
Funding
- NSF CISE Grant [1302668]
- Basque Government [IT685-13]
- CICYT Grant [TIN2011-28169-C05-04]
- Direct For Computer & Info Scie & Enginr [1302668] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems [1302668] Funding Source: National Science Foundation
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Automatic detection of emotions like sarcasm or nastiness in online written conversation is a difficult task. It requires a system that can manage some kind of knowledge to interpret that emotional language is being used. In this work, we try to provide this knowledge to the system by considering alternative sets of features obtained according to different criteria. We test a range of different feature sets using two different classifiers. Our results show that the sarcasm detection task benefits from the inclusion of linguistic and semantic information sources, while nasty language is more easily detected using only a set of surface patterns or indicators. (C) 2014 Elsevier B.V. All rights reserved.
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