Determining Term Subjectivity and Term Orientation for Opinion Mining

Determining Term Subjectivity and Term Orientation for Opinion Mining Andrea Esuli1 and Fabrizio Sebastiani2 (1) Istituto di Scienza e Tecnologie dell’Informazione – Consiglio Nazionale delle Ricerche Via G Moruzzi, 1 – 56124 Pisa, Italy andrea.esuli@isti.cnr.it (2) Dipartimento di Matematica Pura e Applicata – Universit`a di Padova Via GB Belzoni, 7 – 35131 Padova, Italy fabrizio.sebastiani@unipd.it Abstract Opinion mining is a recent subdiscipline of computational linguistics which is con- cerned 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 “subjec- tive” terms contained in text, i.e. decid- ing whether a term that carries opinion- ated 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 realis- tic problem, since it starts from the non- realistic 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 confront the task of de- ciding whether a given term has a positive connotation, or a negative connotation, or has no subjective connotation at all; this problem thus subsumes the problem of de- termining subjectivity and the problem of determining orientation. We tackle this problem by testing three different variants of a semi-supervised method previously proposed for orientation detection. Our results show that determining subjectivity and orientation is a much harder problem than determining orientation alone. 1 Introduction Opinion mining is a recent subdiscipline of com- putational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. Opinion-driven content management has several important applications, such as deter- mining critics’ opinions about a given product by classifying online product reviews, or tracking the shifting attitudes of the general public toward a po- litical candidate by mining online forums. Within opinion mining, several subtasks can be identified, all of them having to do with tagging a given document according to expressed opinion: 1. determining document subjectivity, as in de- ciding whether a given text has a factual na- ture (i.e. describes a given situation or event, without expressing a positive or a negative opinion on it) or expresses an opinion on its subject matter. This amounts to performing binary text categorization under categories Objective and Subjective (Pang and Lee, 2004; Yu and Hatzivassiloglou, 2003); 2. determining document orientation (or polar- ity), as in deciding if a given Subjective text expresses a Positive or a Negative opinion on its subject matter (Pang and Lee, 2004; Turney, 2002); 3. determining the strength of document orien- tation, as in deciding e.g. whether the Posi- tive opinion expressed by a text …

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