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Argamon S., Bloom K., Esuli A., Sebastiani F. Automatically determining attitude type and force for sentiment analysis. Hans Uszkoreit, Zygmunt Vetulani (eds.). (Lecture Notes in Computer Science, vol. 5603). Heidelberg: Springer Verlag, 2009.
 
 
Abstract
(English)
Recent work in sentiment analysis has begun to apply fine-grained semantic distinctions between expressions of attitude as features for textual analysis. Such methods, however, require the construction of large and complex lexicons, giving values for multiple sentiment-related attributes to many different lexical items. For example, a key attribute is what type of attitude is expressed by a lexical item; e.g., beautiful expresses appreciation of an object's quality, while evil expresses a negative judgement of social behavior. In this paper we describe a method for the automatic determination of complex sentiment-related attributes such as attitude type and force, by applying supervised learning to WordNet glosses. Experimental results show that the method achieves good effectiveness, and is therefore well-suited to contexts in which these lexicons need to be generated from scratch.
URL: http://www.springerlink.com/content/t558r0605360/?sortorder=asc&p_o=10
DOI: 10.1007/978-3-642-04235-5_19
Subject Appraisal theory
Sentiment analisys
Term classification
I.2.7 Natural Language Processing. Language models


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