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A language-model-based approach for subjectivity detection

Authors:

Samaneh Karimi,

Azadeh ShakeryAuthors Info & Claims

Journal of Information Science, Volume 43, Issue 3

Pages 356 - 377

https://doi.org/10.1177/0165551516641818

Published: 01 June 2017 Publication History

Abstract

The rapid growth of opinionated text on the Web increases the demand for efficient methods for detecting subjective texts. In this paper, a subjectivity detection method is proposed which utilizes a language-model-based structure to define a subjectivity score for each document where the topic relevance of documents does not affect the subjectivity scores. In order to overcome the limited content in short documents, we further propose an expansion method to better estimate the language models. Since the lack of linguistic resources in resource-lean languages like Persian makes subjectivity detection difficult in these languages, the method is proposed in two versions: a semi-supervised version for resource-lean languages and a supervised version. Experimental evaluations on five datasets in two languages, English and Persian, demonstrate that the method performs well in distinguishing subjective documents from objective ones in both languages.

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Cited By

Karimi SShakery AVerma R(2021)Online news media website ranking using user-generated contentJournal of Information Science10.1177/016555151989492847:3(340-358)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1177/0165551519894928
Barraza NMoro SFerreyra Mde la Peña A(2020)Mutual information and sensitivity analysis for feature selection in customer targetingJournal of Information Science10.1177/016555151877096745:1(53-67)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518770967
Ziser YKravi ECarmel DHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Humor Detection in Product Question Answering SystemsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401077(519-528)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401077
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A language-model-based approach for subjectivity detection
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cover image Journal of Information Science

Journal of Information Science Volume 43, Issue 3

6 2017

141 pages

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Copyright © © The Authors 2016.

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Sage Publications, Inc.

United States

Publication History

Published: 01 June 2017

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Cited By

Karimi SShakery AVerma R(2021)Online news media website ranking using user-generated contentJournal of Information Science10.1177/016555151989492847:3(340-358)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1177/0165551519894928
Barraza NMoro SFerreyra Mde la Peña A(2020)Mutual information and sensitivity analysis for feature selection in customer targetingJournal of Information Science10.1177/016555151877096745:1(53-67)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518770967
Ziser YKravi ECarmel DHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Humor Detection in Product Question Answering SystemsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401077(519-528)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401077
Yue LChen WLi XZuo WYin M(2019)A survey of sentiment analysis in social mediaKnowledge and Information Systems10.1007/s10115-018-1236-460:2(617-663)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10115-018-1236-4

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