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SentiWordNet is a lexical resource in which each WordNet synset is associated to three numerical scores Obj(s), Pos(s) and Neg(s), describing how objective, positive, and negative the terms contained in the synset are.
A typical use of SentiWordNet is to enrich the text representation in opinion mining (OM) applications, adding information on the sentiment-related properties of the terms in text. OM is a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. OM has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management.
In order to aid the extraction of opinions from text, recent research has tried to automatically determine the 'PN-polarity' of subjective terms, i.e. identify whether a term that is a marker of opinionated content has a positive or a negative connotation. Research on determining whether a term is indeed a marker of opinionated content (a subjective term) or not (an objective term) has been, instead, much more scarce. SentiWordNet is the first lexical resource which provide such specific level of detail (the word sense represented by a synset) and such broad coverage (all the 115,000+ WordNet synsets).
The method used to develop SentiWordNet is based on the quantitative analysis of the glosses associated to synsets, and on the use of the resulting vectorial term representations for semi-supervised synset classification. The three scores are derived by combining the results produced by a committee of eight ternary classifiers, all characterized by similar accuracy levels but different classification behaviour.
Access SentiWordNet online
Last modified: Tue Aug 28 2007