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Imbalanced sentiment classification

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Rangyang WangAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2469 - 2472

https://doi.org/10.1145/2063576.2063994

Published: 24 October 2011 Publication History

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Abstract

Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.

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Cited By

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Al Mahmoud RHammo BFaris H(2023)Cluster-based ensemble learning model for improving sentiment classification of Arabic documentsNatural Language Engineering10.1017/S135132492300027X(1-39)Online publication date: 1-Jun-2023
https://doi.org/10.1017/S135132492300027X
Li DChen SLin YHsu W(2022)A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text DataSymmetry10.3390/sym1403056714:3(567)Online publication date: 13-Mar-2022
https://doi.org/10.3390/sym14030567
Prisdayanti ABudi ISantoso APutra P(2022)Sentiment analysis of government regulations regarding the implementation of reverse-transcriptase polymerase chain reaction (RT-PCR) during the Covid-19 pandemic in Indonesia (Case study: Air transportation mode)THE 2ND INTERNATIONAL CONFERENCE OF SCIENCE AND INFORMATION TECHNOLOGY IN SMART ADMINISTRATION (ICSINTESA 2021)10.1063/5.0107573(090002)Online publication date: 2022
https://doi.org/10.1063/5.0107573
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Index Terms

Imbalanced sentiment classification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2011

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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

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Cited By

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Al Mahmoud RHammo BFaris H(2023)Cluster-based ensemble learning model for improving sentiment classification of Arabic documentsNatural Language Engineering10.1017/S135132492300027X(1-39)Online publication date: 1-Jun-2023
https://doi.org/10.1017/S135132492300027X
Li DChen SLin YHsu W(2022)A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text DataSymmetry10.3390/sym1403056714:3(567)Online publication date: 13-Mar-2022
https://doi.org/10.3390/sym14030567
Prisdayanti ABudi ISantoso APutra P(2022)Sentiment analysis of government regulations regarding the implementation of reverse-transcriptase polymerase chain reaction (RT-PCR) during the Covid-19 pandemic in Indonesia (Case study: Air transportation mode)THE 2ND INTERNATIONAL CONFERENCE OF SCIENCE AND INFORMATION TECHNOLOGY IN SMART ADMINISTRATION (ICSINTESA 2021)10.1063/5.0107573(090002)Online publication date: 2022
https://doi.org/10.1063/5.0107573
Wang CYang XDing L(2021)Sentiment classification based on weak tagging information and imbalanced dataIntelligent Data Analysis10.3233/IDA-20540825:3(555-570)Online publication date: 20-Apr-2021
https://doi.org/10.3233/IDA-205408
Polpinij JNamee K(2021)Improving of Imbalanced Data in Multiclass Classification for Sentiment Analysis using Supervised Term Weighting2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics (RI2C)10.1109/RI2C51727.2021.9559797(19-24)Online publication date: 1-Sep-2021
https://doi.org/10.1109/RI2C51727.2021.9559797
Wang CYang XDing L(2021)Deep Learning Sentiment Classification Based on Weak Tagging InformationIEEE Access10.1109/ACCESS.2021.30770599(66509-66518)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3077059
Pavan Kumar MJayagopal P(2021)Context-sensitive lexicon for imbalanced text sentiment classification using bidirectional LSTMJournal of Intelligent Manufacturing10.1007/s10845-021-01866-034:5(2123-2132)Online publication date: 10-Nov-2021
https://doi.org/10.1007/s10845-021-01866-0
Liu YBoukouvalas ZJapkowicz N(2021)A Semi-supervised Framework for Misinformation DetectionDiscovery Science10.1007/978-3-030-88942-5_5(57-66)Online publication date: 11-Oct-2021
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Wang CYang XDing L(2020)Imbalanced sentiment classification based on sequence generative adversarial netsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-201370(1-11)Online publication date: 27-Aug-2020
https://doi.org/10.3233/JIFS-201370
Lango M(2019)Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental StudyFoundations of Computing and Decision Sciences10.2478/fcds-2019-000944:2(151-178)Online publication date: 6-Jun-2019
https://doi.org/10.2478/fcds-2019-0009
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