Automatically determining attitude type and force for sentiment analysis

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.