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Which side are you on?: identifying perspectives at the document and sentence levels

Authors:

Theresa Wilson,

Alexander HauptmannAuthors Info & Claims

CoNLL-X '06: Proceedings of the Tenth Conference on Computational Natural Language Learning

Pages 109 - 116

Published: 08 June 2006 Publication History

Abstract

In this paper we investigate a new problem of identifying the perspective from which a document is written. By perspective we mean a point of view, for example, from the perspective of Democrats or Republicans. Can computers learn to identify the perspective of a document? Not every sentence is written strongly from a perspective. Can computers learn to identify which sentences strongly convey a particular perspective? We develop statistical models to capture how perspectives are expressed at the document and sentence levels, and evaluate the proposed models on articles about the Israeli-Palestinian conflict. The results show that the proposed models successfully learn how perspectives are reflected in word usage and can identify the perspective of a document with high accuracy.

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Cited By

Tian FWu FFei XShah NZheng QWang Y(2019)Improving generalization ability of instance transfer-based imbalanced sentiment classification of turn-level interactive Chinese textsService Oriented Computing and Applications10.1007/s11761-019-00264-y13:2(155-167)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11761-019-00264-y
Van Hee CLefever EHoste V(2018)We usually don't like going to the dentistComputational Linguistics10.1162/coli_a_0033744:4(793-832)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1162/coli_a_00337
Fuhr NGiachanou AGrefenstette GGurevych IHanselowski AJarvelin KJones RLiu YMothe JNejdl WPeters IStein B(2018)An Information Nutritional Label for Online DocumentsACM SIGIR Forum10.1145/3190580.319058851:3(46-66)Online publication date: 22-Feb-2018
https://dl.acm.org/doi/10.1145/3190580.3190588
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Which side are you on?: identifying perspectives at the document and sentence levels
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Published In

cover image DL Hosted proceedings

CoNLL-X '06: Proceedings of the Tenth Conference on Computational Natural Language Learning

June 2006

266 pages

Program Chairs:
Lluis Marquez
Technical University of Catalonia, Spain
,
Dan Klein
University of California at Berkeley

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 08 June 2006

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Cited By

Tian FWu FFei XShah NZheng QWang Y(2019)Improving generalization ability of instance transfer-based imbalanced sentiment classification of turn-level interactive Chinese textsService Oriented Computing and Applications10.1007/s11761-019-00264-y13:2(155-167)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11761-019-00264-y
Van Hee CLefever EHoste V(2018)We usually don't like going to the dentistComputational Linguistics10.1162/coli_a_0033744:4(793-832)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1162/coli_a_00337
Fuhr NGiachanou AGrefenstette GGurevych IHanselowski AJarvelin KJones RLiu YMothe JNejdl WPeters IStein B(2018)An Information Nutritional Label for Online DocumentsACM SIGIR Forum10.1145/3190580.319058851:3(46-66)Online publication date: 22-Feb-2018
https://dl.acm.org/doi/10.1145/3190580.3190588
Ghosh SAnand KRajanala SReddy ASingh MRanu SGanguly NRamakrishnan RSarawagi SRoy S(2018)Unsupervised stance classification in online debatesProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3152494.3152497(30-36)Online publication date: 11-Jan-2018
https://dl.acm.org/doi/10.1145/3152494.3152497
(2017)Fine-grained opinion mining of product review using sentiment and semantic orientationInternational Journal of Business Information Systems10.1504/IJBIS.2017.08327425:1(1-17)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1504/IJBIS.2017.083274
Lin JMao WZhang YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)An Enhanced Topic Modeling Approach to Multiple Stance IdentificationProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133145(2167-2170)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133145
Addawood ASchneider JBashir M(2017)Stance Classification of Twitter DebatesProceedings of the 8th International Conference on Social Media & Society10.1145/3097286.3097288(1-10)Online publication date: 28-Jul-2017
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Yadollahi AShahraki AZaiane O(2017)Current State of Text Sentiment Analysis from Opinion to Emotion MiningACM Computing Surveys10.1145/305727050:2(1-33)Online publication date: 25-May-2017
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Yan ZJing XPedrycz W(2017)Fusing and mining opinions for reputation generationInformation Fusion10.1016/j.inffus.2016.11.01136:C(172-184)Online publication date: 1-Jul-2017
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Dubey GRana ARanjan J(2016)A research study of sentiment analysis and various techniques of sentiment classificationInternational Journal of Data Analysis Techniques and Strategies10.1504/IJDATS.2016.0774858:2(122-142)Online publication date: 1-Jan-2016
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