Determining Term Subjectivity and Term Orientation for Opinion Mining

Opinion mining is a recent subdiscipline of computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of "subjective" terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a negative connotation. This is believed to be of key importance for identifying the orientation of documents, i.e. determining whether a document expresses a positive or negative opinion about its subject matter. We contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the nonrealistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as "subjective" or "objective" is available, which is usually not the case. In this paper we con...

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Andrea Esuli, Fabrizio Sebastiani
• Keywords: Computational Linguistics, EACL 2006, Natural Language Processing, Negative Connotation, Opinion Mining