Argamon S., Bloom K., Esuli A., Sebastiani F.
I.2.7 Natural Language Processing. Language models Appraisal theory Term classification Sentiment analisys
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.
Source: Lecture notes in computer science 5603 (2009): 218–231. doi:10.1007/978-3-642-04235-5_19
Publisher: Springer, Berlin , Germania
@article{oai:it.cnr:prodotti:44261, title = {Automatically determining attitude type and force for sentiment analysis}, author = {Argamon S. and Bloom K. and Esuli A. and Sebastiani F.}, publisher = {Springer, Berlin , Germania}, doi = {10.1007/978-3-642-04235-5_19}, journal = {Lecture notes in computer science}, volume = {5603}, pages = {218–231}, year = {2009} }