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SELC: a self-supervised model for sentiment classification

Authors:

Kai ZhaoAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 929 - 936

https://doi.org/10.1145/1645953.1646072

Published: 02 November 2009 Publication History

Abstract

This paper presents the SELC Model (SElf-Supervised, (Lexicon-based and (Corpus-based Model) for sentiment classification. The SELC Model includes two phases. The first phase is a lexicon-based iterative process. In this phase, some reviews are initially classified based on a sentiment dictionary. Then more reviews are classified through an iterative process with a negative/positive ratio control. In the second phase, a supervised classifier is learned by taking some reviews classified in the first phase as training data. Then the supervised classifier applies on other reviews to revise the results produced in the first phase. Experiments show the effectiveness of the proposed model. SELC totally achieves 6.63% F₁-score improvement over the best result in previous studies on the same data (from 82.72% to 89.35%). The first phase of the SELC Model independently achieves 5.90% improvement (from 82.72% to 88.62%). Moreover, the standard deviation of F₁-scores is reduced, which shows that the SELC Model could be more suitable for domain-independent sentiment classification.

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Cited By

Alizadeh MSeilsepour A(2024)A novel self-supervised sentiment classification approach using semantic labeling based on contextual embeddingsMultimedia Tools and Applications10.1007/s11042-024-19086-yOnline publication date: 13-May-2024
https://doi.org/10.1007/s11042-024-19086-y
Seilsepour ARavanmehr RNassiri R(2023)SSTSA: A Self-Supervised Topic Sentiment Analysis Using Semantic Similarity Measures and TransformersInternational Journal of Information Technology & Decision Making10.1142/S021962202350073623:06(2269-2307)Online publication date: 2-Aug-2023
https://doi.org/10.1142/S0219622023500736
Seilsepour AAlizadeh MRavanmehr RBeheshti MNassiri R(2022)Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)10.1109/ICSPIS56952.2022.10043914(1-7)Online publication date: 28-Dec-2022
https://doi.org/10.1109/ICSPIS56952.2022.10043914
Show More Cited By

Index Terms

SELC: a self-supervised model for sentiment classification
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

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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Published: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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

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Cited By

Alizadeh MSeilsepour A(2024)A novel self-supervised sentiment classification approach using semantic labeling based on contextual embeddingsMultimedia Tools and Applications10.1007/s11042-024-19086-yOnline publication date: 13-May-2024
https://doi.org/10.1007/s11042-024-19086-y
Seilsepour ARavanmehr RNassiri R(2023)SSTSA: A Self-Supervised Topic Sentiment Analysis Using Semantic Similarity Measures and TransformersInternational Journal of Information Technology & Decision Making10.1142/S021962202350073623:06(2269-2307)Online publication date: 2-Aug-2023
https://doi.org/10.1142/S0219622023500736
Seilsepour AAlizadeh MRavanmehr RBeheshti MNassiri R(2022)Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)10.1109/ICSPIS56952.2022.10043914(1-7)Online publication date: 28-Dec-2022
https://doi.org/10.1109/ICSPIS56952.2022.10043914
Sazzed SJayarathna S(2021)SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled dataMachine Learning with Applications10.1016/j.mlwa.2021.100026(100026)Online publication date: Mar-2021
https://doi.org/10.1016/j.mlwa.2021.100026
Yalkun ESlamu WTurhuntay R(2020)A mixed approach of statistical weighting method and unsupervised method to improve Uyghur sentiment classificationJournal of Computational Methods in Sciences and Engineering10.3233/JCM-204645(1-23)Online publication date: 24-Sep-2020
https://doi.org/10.3233/JCM-204645
Liu B(2020)Sentiment Analysis10.1017/9781108639286Online publication date: 23-Sep-2020
https://doi.org/10.1017/9781108639286
Li YLv YWang SLiang JLi JLi X(2019)Cooperative Hybrid Semi-Supervised Learning for Text Sentiment ClassificationSymmetry10.3390/sym1102013311:2(133)Online publication date: 24-Jan-2019
https://doi.org/10.3390/sym11020133
Khan JLee Y(2019)LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment ClassificationApplied Sciences10.3390/app92455629:24(5562)Online publication date: 17-Dec-2019
https://doi.org/10.3390/app9245562
Jin JLiu YJi PKwong C(2018)Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product DesignJournal of Computing and Information Science in Engineering10.1115/1.404108719:1Online publication date: 17-Sep-2018
https://doi.org/10.1115/1.4041087
Dragoni MPetrucci G(2018)A fuzzy-based strategy for multi-domain sentiment analysisInternational Journal of Approximate Reasoning10.1016/j.ijar.2017.10.02193:C(59-73)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.ijar.2017.10.021
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