research-article

Polarity shift detection, elimination and ensemble

Authors:

Erik CambriaAuthors Info & Claims

Information Processing and Management: an International Journal, Volume 52, Issue 1

Pages 36 - 45

https://doi.org/10.1016/j.ipm.2015.04.003

Published: 01 January 2016 Publication History

Abstract

The polarity shift problem is a major factor that affects classification performance of machine-learning-based sentiment analysis systems. In this paper, we propose a three-stage cascade model to address the polarity shift problem in the context of document-level sentiment classification. We first split each document into a set of subsentences and build a hybrid model that employs rules and statistical methods to detect explicit and implicit polarity shifts, respectively. Secondly, we propose a polarity shift elimination method, to remove polarity shift in negations. Finally, we train base classifiers on training subsets divided by different types of polarity shifts, and use a weighted combination of the component classifiers for sentiment classification. The results on a range of experiments illustrate that our approach significantly outperforms several alternative methods for polarity shift detection and elimination.

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Cited By

Wang JYuan JLiu W(2023)Sentiment Analysis and Corpus: Cognitive Perspective and Overhead-accuracy TradeoffACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359453723:6(1-17)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3594537
Tang XLiao DShen MZhu LHuang SLi GMan HXu J(2023)Confidence-Aware Sentiment Quantification via Sentiment Perturbation ModelingIEEE Transactions on Affective Computing10.1109/TAFFC.2023.330195615:2(736-750)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1109/TAFFC.2023.3301956
Ayeste ZNoferesti S(2022)A semantic approach based on domain knowledge for polarity shift detection using distant supervisionProgress in Artificial Intelligence10.1007/s13748-021-00267-x11:2(169-180)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s13748-021-00267-x
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Polarity shift detection, elimination and ensemble
1. Computing methodologies
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2. Hardware
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cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 52, Issue 1

January 2016

173 pages

ISSN:0306-4573

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2016

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Cited By

Wang JYuan JLiu W(2023)Sentiment Analysis and Corpus: Cognitive Perspective and Overhead-accuracy TradeoffACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359453723:6(1-17)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3594537
Tang XLiao DShen MZhu LHuang SLi GMan HXu J(2023)Confidence-Aware Sentiment Quantification via Sentiment Perturbation ModelingIEEE Transactions on Affective Computing10.1109/TAFFC.2023.330195615:2(736-750)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1109/TAFFC.2023.3301956
Ayeste ZNoferesti S(2022)A semantic approach based on domain knowledge for polarity shift detection using distant supervisionProgress in Artificial Intelligence10.1007/s13748-021-00267-x11:2(169-180)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s13748-021-00267-x
Pandey AKulhari AMittal HTripathi APal R(2022)Improved exponential cuckoo search method for sentiment analysisMultimedia Tools and Applications10.1007/s11042-022-14229-582:16(23979-24029)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-14229-5
Sohrabi MHemmatian F(2022)An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case studyMultimedia Tools and Applications10.1007/s11042-019-7586-478:17(24863-24882)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-019-7586-4
Hemmatian FSohrabi M(2022)A survey on classification techniques for opinion mining and sentiment analysisArtificial Intelligence Review10.1007/s10462-017-9599-652:3(1495-1545)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s10462-017-9599-6
Prasanna MShaila SVadivel A(2021)Phrase-level sentence patterns for estimating positive and negative emotions using Neuro-fuzzy model for information retrieval applicationsMultimedia Tools and Applications10.1007/s11042-020-10422-680:13(20151-20190)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s11042-020-10422-6
Hung CWu WChou H(2021)Improvement of sentiment analysis via re-evaluation of objective words in SenticNet for hotel reviewsLanguage Resources and Evaluation10.1007/s10579-020-09512-655:2(585-595)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10579-020-09512-6
AL-Sharuee MLiu FPratama M(2021)Sentiment analysis: dynamic and temporal clustering of product reviewsApplied Intelligence10.1007/s10489-020-01668-651:1(51-70)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10489-020-01668-6
Yang GHe HChen Q(2019)Emotion-Semantic-Enhanced Neural NetworkIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2018.288577527:3(531-543)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TASLP.2018.2885775
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