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Automatically Determining Attitude Type and Force for Sentiment Analysis Shlomo Argamon
 

Summary: Automatically Determining Attitude Type and Force for Sentiment Analysis
Shlomo Argamon
, Kenneth Bloom
, Andrea Esuli
, Fabrizio Sebastiani

Linguistic Cognition Laboratory Department of Computer Science
Illinois Institute of Technology 10 W. 31st Street Chicago, IL 60616, USA
{argamon,kbloom1}@iit.edu

Istituto di Scienza e Tecnologie dell'Informazione Consiglio Nazionale delle Ricerche
Via G Moruzzi, 1 56124 Pisa, Italy
{andrea.esuli,fabrizio.sebastiani}@isti.cnr.it
Abstract
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

  

Source: Argamon, Shlomo - Department of Computer Science, Illinois Institute of Technology

 

Collections: Computer Technologies and Information Sciences