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Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification

Authors:

Danushka Bollegala,

John CarrollAuthors Info & Claims

HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1

Pages 132 - 141

Published: 19 June 2011 Publication History

Abstract

We describe a sentiment classification method that is applicable when we do not have any labeled data for a target domain but have some labeled data for multiple other domains, designated as the source domains. We automatically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to find the association between words that express similar sentiments in different domains. The created thesaurus is then used to expand feature vectors to train a binary classifier. Unlike previous cross-domain sentiment classification methods, our method can efficiently learn from multiple source domains. Our method significantly outperforms numerous baselines and returns results that are better than or comparable to previous cross-domain sentiment classification methods on a benchmark dataset containing Amazon user reviews for different types of products.

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Cited By

Tang YHuang H(2021)A Teacher-Student Approach to Cross-Domain Transfer Learning with Multi-level AttentionIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3494009(494-499)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3494009
Kayal PSingh MGoyal PVarma VKambhampati SBhattacharya ANatarajan SRoy R(2020)Weakly-Supervised Deep Learning for Domain Invariant Sentiment ClassificationProceedings of the 7th ACM IKDD CoDS and 25th COMAD10.1145/3371158.3371194(239-243)Online publication date: 5-Jan-2020
https://dl.acm.org/doi/10.1145/3371158.3371194
Tran N(2018)Aspect Based Sentiment Analysis Using NeuroNER and Bidirectional Recurrent Neural NetworkProceedings of the 9th International Symposium on Information and Communication Technology10.1145/3287921.3287922(1-7)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3287921.3287922
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Index Terms

Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification
1. Applied computing
  1. Arts and humanities
    1. Language translation
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1

June 2011

1696 pages

ISBN:9781932432879

General Chair:
Dekang Lin
Google

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 19 June 2011

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Overall Acceptance Rate 240 of 768 submissions, 31%

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Cited By

Tang YHuang H(2021)A Teacher-Student Approach to Cross-Domain Transfer Learning with Multi-level AttentionIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3494009(494-499)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3494009
Kayal PSingh MGoyal PVarma VKambhampati SBhattacharya ANatarajan SRoy R(2020)Weakly-Supervised Deep Learning for Domain Invariant Sentiment ClassificationProceedings of the 7th ACM IKDD CoDS and 25th COMAD10.1145/3371158.3371194(239-243)Online publication date: 5-Jan-2020
https://dl.acm.org/doi/10.1145/3371158.3371194
Tran N(2018)Aspect Based Sentiment Analysis Using NeuroNER and Bidirectional Recurrent Neural NetworkProceedings of the 9th International Symposium on Information and Communication Technology10.1145/3287921.3287922(1-7)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3287921.3287922
Carstens LToni F(2017)Using Argumentation to Improve Classification in Natural Language ProblemsACM Transactions on Internet Technology10.1145/301767917:3(1-23)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3017679
Bollegala DMu TGoulermas J(2016)Cross-Domain Sentiment Classification Using Sentiment Sensitive EmbeddingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.247576128:2(398-410)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2475761
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
Cui ZShi XChen Y(2016)Sentiment analysis via integrating distributed representations of variable-length word sequenceNeurocomputing10.1016/j.neucom.2015.07.129187:C(126-132)Online publication date: 26-Apr-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.07.129
Rao YXie HLi JJin FWang FLi Q(2016)Social emotion classification of short text via topic-level maximum entropy modelInformation and Management10.1016/j.im.2016.04.00553:8(978-986)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.im.2016.04.005
Zhou GHe TWu WHu X(2015)Linking heterogeneous input features with pivots for domain adaptationProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832415.2832447(1419-1425)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832415.2832447
Quan XWang QZhang YSi LWenyin L(2015)Latent Discriminative Models for Social Emotion Detection with Emotional DependencyACM Transactions on Information Systems10.1145/274945934:1(1-19)Online publication date: 28-Jul-2015
https://dl.acm.org/doi/10.1145/2749459
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