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Improving the impact of subjectivity word sense disambiguation on contextual opinion analysis

Authors:

Alexander Conrad,

Rada MihalceaAuthors Info & Claims

CoNLL '11: Proceedings of the Fifteenth Conference on Computational Natural Language Learning

Pages 87 - 96

Published: 23 June 2011 Publication History

Abstract

Subjectivity word sense disambiguation (SWSD) is automatically determining which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. SWSD has been shown to improve the performance of contextual opinion analysis, but only on a small scale and using manually developed integration rules. In this paper, we scale up the integration of SWSD into contextual opinion analysis and still obtain improvements in performance, by successfully gathering data annotated by non-expert annotators. Further, by improving the method for integrating SWSD into contextual opinion analysis, even greater benefits from SWSD are achieved than in previous work. We thus more firmly demonstrate the potential of SWSD to improve contextual opinion analysis.

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Cited By

Novielli NCalefato FLanubile FHammouda ISillitti A(2015)The challenges of sentiment detection in the social programmer ecosystemProceedings of the 7th International Workshop on Social Software Engineering10.1145/2804381.2804387(33-40)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1145/2804381.2804387
Trindade LWang HBlackburn WRooney N(2013)Factored semantic sequence kernel for sentiment polarity classificationProceedings of the First international conference on Statistical Language and Speech Processing10.1007/978-3-642-39593-2_25(284-296)Online publication date: 29-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39593-2_25
Trindade LWang HBlackburn WRooney N(2013)An enhanced semantic tree kernel for sentiment polarity classificationProceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 210.1007/978-3-642-37256-8_5(50-62)Online publication date: 24-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37256-8_5
Show More Cited By

Index Terms

Improving the impact of subjectivity word sense disambiguation on contextual opinion analysis
1. Applied computing
  1. Arts and humanities
    1. Language translation
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image DL Hosted proceedings

CoNLL '11: Proceedings of the Fifteenth Conference on Computational Natural Language Learning

June 2011

270 pages

ISBN:9781932432923

Program Chairs:
Sharon Goldwater
University of Edinburgh, United Kingdom
,
Christopher Manning
Stanford University

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 23 June 2011

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Cited By

Novielli NCalefato FLanubile FHammouda ISillitti A(2015)The challenges of sentiment detection in the social programmer ecosystemProceedings of the 7th International Workshop on Social Software Engineering10.1145/2804381.2804387(33-40)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1145/2804381.2804387
Trindade LWang HBlackburn WRooney N(2013)Factored semantic sequence kernel for sentiment polarity classificationProceedings of the First international conference on Statistical Language and Speech Processing10.1007/978-3-642-39593-2_25(284-296)Online publication date: 29-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39593-2_25
Trindade LWang HBlackburn WRooney N(2013)An enhanced semantic tree kernel for sentiment polarity classificationProceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 210.1007/978-3-642-37256-8_5(50-62)Online publication date: 24-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37256-8_5
Das AGambäck BBalahur AMontoyo AMartínez-Barco PBoldrini E(2012)SentimanticsProceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis10.5555/2392963.2392974(38-46)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2392963.2392974

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