Determining term subjectivity and term orientation for opinion mining

Andrea Esuli, Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

163 Citations (Scopus)

Abstract

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 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 deciding 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 determining 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.

Original languageEnglish
Title of host publicationEACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Pages193-200
Number of pages8
Publication statusPublished - 2006
Externally publishedYes
Event11th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2006 - Trento, Italy
Duration: 3 Apr 20067 Apr 2006

Other

Other11th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2006
CountryItaly
CityTrento
Period3/4/067/4/06

Fingerprint

subjectivity
computational linguistics
Subjectivity
linguistics
resources
Negative Connotations

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Esuli, A., & Sebastiani, F. (2006). Determining term subjectivity and term orientation for opinion mining. In EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 193-200)

Determining term subjectivity and term orientation for opinion mining. / Esuli, Andrea; Sebastiani, Fabrizio.

EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. 2006. p. 193-200.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Esuli, A & Sebastiani, F 2006, Determining term subjectivity and term orientation for opinion mining. in EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. pp. 193-200, 11th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2006, Trento, Italy, 3/4/06.
Esuli A, Sebastiani F. Determining term subjectivity and term orientation for opinion mining. In EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. 2006. p. 193-200
Esuli, Andrea ; Sebastiani, Fabrizio. / Determining term subjectivity and term orientation for opinion mining. EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. 2006. pp. 193-200
@inproceedings{4cf3688116824e6eb645963ecd1a2747,
title = "Determining term subjectivity and term orientation for opinion mining",
abstract = "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 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 deciding 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 determining 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.",
author = "Andrea Esuli and Fabrizio Sebastiani",
year = "2006",
language = "English",
isbn = "1932432590",
pages = "193--200",
booktitle = "EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference",

}

TY - GEN

T1 - Determining term subjectivity and term orientation for opinion mining

AU - Esuli, Andrea

AU - Sebastiani, Fabrizio

PY - 2006

Y1 - 2006

N2 - 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 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 deciding 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 determining 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.

AB - 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 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 deciding 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 determining 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.

UR - http://www.scopus.com/inward/record.url?scp=84893427183&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893427183&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84893427183

SN - 1932432590

SN - 9781932432596

SP - 193

EP - 200

BT - EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

ER -