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Article

Active learning for cross-domain sentiment classification

Authors:

Zhongqing Wang,

Guodong ZhouAuthors Info & Claims

IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence

Pages 2127 - 2133

Published: 03 August 2013 Publication History

Abstract

In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). However, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. In this paper, we suggest to perform active learning for cross-domain sentiment classification by actively selecting a small amount of labeled data in the target domain. Accordingly, we propose an novel active learning approach for cross-domain sentiment classification. First, we train two individual classifiers, i.e., the source and target classifiers with the labeled data from the source and target respectively. Then, the two classifiers are employed to select informative samples with the selection strategy of Query By Committee (QBC). Third, the two classifier is combined to make the classification decision. Importantly, the two classifiers are trained by fully exploiting the unlabeled data in the target domain with the label propagation (LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domain sentiment classification over some strong baselines.

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Cited By

Khan FQamar UBashir S(2019)Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3187-923:14(5431-5442)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3187-9
Li JLi HKang XYang HZong C(2018)Incorporating Multi-Level User Preference into Document-Level Sentiment ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/323451218:1(1-17)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3234512
Guo YDing GGao YHan JSingh SMarkovitch S(2017)Active learning with cross-class similarity transferProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298435(1338-1344)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298435
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Published In

cover image Guide Proceedings

IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence

August 2013

3266 pages

ISBN:9781577356332

Editor:
Francesca Rossi
University of Padova

Sponsors

The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)

Publisher

AAAI Press

Publication History

Published: 03 August 2013

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Cited By

Khan FQamar UBashir S(2019)Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3187-923:14(5431-5442)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3187-9
Li JLi HKang XYang HZong C(2018)Incorporating Multi-Level User Preference into Document-Level Sentiment ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/323451218:1(1-17)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3234512
Guo YDing GGao YHan JSingh SMarkovitch S(2017)Active learning with cross-class similarity transferProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298435(1338-1344)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298435
Wang ZDu BZhang LZhang LHu RTao D(2017)On gleaning knowledge from multiple domains for active learningProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172309(3013-3019)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172309
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
https://dl.acm.org/doi/10.1145/3057270
Huang SChen S(2016)Transfer learning with active queries from source domainProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060843(1592-1598)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060843
Guo YDing GGao YWang J(2016)Semi-supervised active learning with cross-class sample transferProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060834(1526-1532)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060834
Guo YDing GWang YJin X(2016)Active learning with cross-class knowledge transferProceedings of the Thirtieth AAAI Conference on Artificial Intelligence10.5555/3016100.3016126(1624-1630)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.5555/3016100.3016126
Wu FWu SHuang YHuang SQin YMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Sentiment Domain Adaptation with Multi-Level Contextual Sentiment KnowledgeProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983851(949-958)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983851
Liang JZhang KZhou XHu YTan JBai S(2016)Leveraging Latent Sentiment Constraint in Probabilistic Matrix Factorization for Cross-domain Sentiment ClassificationProcedia Computer Science10.1016/j.procs.2016.05.35380:C(366-375)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.05.353
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