research-article

A partially supervised cross-collection topic model for cross-domain text classification

Authors:

Anindya DattaAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 239 - 248

https://doi.org/10.1145/2505515.2505556

Published: 27 October 2013 Publication History

Abstract

Cross-domain text classification aims to automatically train a precise text classifier for a target domain by using labelled text data from a related source domain. To this end, one of the most promising ideas is to induce a new feature representation so that the distributional difference between domains can be reduced and a more accurate classifier can be learned in this new feature space. However, most existing methods do not explore the duality of the marginal distribution of examples and the conditional distribution of class labels given labeled training examples in the source domain. Besides, few previous works attempt to explicitly distinguish the domain-independent and domain-specific latent features and align the domain-specific features to further improve the cross-domain learning. In this paper, we propose a model called Partially Supervised Cross-Collection LDA topic model (PSCCLDA) for cross-domain learning with the purpose of addressing these two issues in a unified way. Experimental results on nine datasets show that our model outperforms two standard classifiers and four state-of-the-art methods, which demonstrates the effectiveness of our proposed model.

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Cited By

Liu TJain DRapole SCurtis BEichstaedt JUngar LGuntuku SHorvát ÁHall WContractor NFröhling LOgnayova KTaneja HWeber IGligori? KMejova Y(2023)Detecting Symptoms of Depression on RedditProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583621(174-183)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3578503.3583621
Rashid JKim JNaseem U(2023)Coherent Topic Modeling for Creative Multimodal Data on Social MediaProceedings of the ACM Web Conference 202310.1145/3543507.3587433(3923-3927)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3587433
Raghunathan NSaravanakumar K(2023)Challenges and Issues in Sentiment Analysis: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2023.329304111(69626-69642)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3293041
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Index Terms

A partially supervised cross-collection topic model for cross-domain text classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013.

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Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Liu TJain DRapole SCurtis BEichstaedt JUngar LGuntuku SHorvát ÁHall WContractor NFröhling LOgnayova KTaneja HWeber IGligori? KMejova Y(2023)Detecting Symptoms of Depression on RedditProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583621(174-183)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3578503.3583621
Rashid JKim JNaseem U(2023)Coherent Topic Modeling for Creative Multimodal Data on Social MediaProceedings of the ACM Web Conference 202310.1145/3543507.3587433(3923-3927)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3587433
Raghunathan NSaravanakumar K(2023)Challenges and Issues in Sentiment Analysis: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2023.329304111(69626-69642)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3293041
Liu NZhao J(2022)A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain TextComputational Intelligence and Neuroscience10.1155/2022/87266212022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8726621
Wang DWu JYang JJing BZhang WHe XZhang H(2022)Cross-Lingual Knowledge Transferring by Structural Correspondence and Space TransferIEEE Transactions on Cybernetics10.1109/TCYB.2021.305100552:7(6555-6566)Online publication date: Jul-2022
https://doi.org/10.1109/TCYB.2021.3051005
Moreo AEsuli ASebastiani F(2021)Lost in Transduction: Transductive Transfer Learning in Text ClassificationACM Transactions on Knowledge Discovery from Data10.1145/345314616:1(1-21)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3453146
Wang DJing BLu CWu JLiu GDu CZhuang F(2021)Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297922532:2(736-747)Online publication date: Feb-2021
https://doi.org/10.1109/TNNLS.2020.2979225
Chen XHan T(2020)A Micro Perspective of Research Dynamics Through “Citations of Citations” Topic AnalysisJournal of Data and Information Science10.2478/jdis-2020-00345:4(19-34)Online publication date: 28-Jul-2020
https://doi.org/10.2478/jdis-2020-0034
Xue FHong RHe XWang JQian SXu C(2020)Knowledge-Based Topic Model for Multi-Modal Social Event AnalysisIEEE Transactions on Multimedia10.1109/TMM.2019.295119422:8(2098-2110)Online publication date: Aug-2020
https://doi.org/10.1109/TMM.2019.2951194
Xue FWang JQian SZhang TLiu XXu C(2019)Multi-modal max-margin supervised topic model for social event analysisMultimedia Tools and Applications10.1007/s11042-017-5605-x78:1(141-160)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11042-017-5605-x
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