research-article

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

Authors:

Sivaji Bandyopadhyay,

f sAuthors Info & Claims

IEEE Intelligent Systems, Volume 28, Issue 2

Pages 31 - 38

https://doi.org/10.1109/MIS.2013.4

Published: 01 March 2013 Publication History

Abstract

SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.

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Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Liu ZZhang TYang KThompson PYu ZAnaniadou S(2024)Emotion detection for misinformationInformation Fusion10.1016/j.inffus.2024.102300107:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102300
Sun JZhang QYuan KJiang YChen X(2024)A supervised contrastive learning-based model for image emotion classificationWorld Wide Web10.1007/s11280-024-01260-927:3Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01260-9
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Published In

IEEE Intelligent Systems Volume 28, Issue 2

March 2013

103 pages

ISSN:1541-1672

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Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 March 2013

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Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Liu ZZhang TYang KThompson PYu ZAnaniadou S(2024)Emotion detection for misinformationInformation Fusion10.1016/j.inffus.2024.102300107:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102300
Sun JZhang QYuan KJiang YChen X(2024)A supervised contrastive learning-based model for image emotion classificationWorld Wide Web10.1007/s11280-024-01260-927:3Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01260-9
Thakur DPal A(2023)Subsampled Randomized Hadamard Transformation-based Ensemble Extreme Learning Machine for Human Activity RecognitionACM Transactions on Computing for Healthcare10.1145/36348135:1(1-23)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3634813
Das RSingh T(2023)Multimodal Sentiment Analysis: A Survey of Methods, Trends, and ChallengesACM Computing Surveys10.1145/358607555:13s(1-38)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3586075
Dong KLiu YXu FLiu P(2023)DCAT: Combining Multisemantic Dual-Channel Attention Fusion for Text ClassificationIEEE Intelligent Systems10.1109/MIS.2023.326822838:4(10-19)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/MIS.2023.3268228
Alsaif HAldossari H(2023)Review of stance detection for rumor verification in social mediaEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105801119:COnline publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105801
Bordoloi MBiswas S(2023)Sentiment analysis: A survey on design framework, applications and future scopesArtificial Intelligence Review10.1007/s10462-023-10442-256:11(12505-12560)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-023-10442-2
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
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Behera RBala PRana NKizgin H(2022)A Techno-Business Platform to Improve Customer Experience Following the Brand Crisis Recovery: A B2B PerspectiveInformation Systems Frontiers10.1007/s10796-021-10231-824:6(2027-2051)Online publication date: 15-Jan-2022
https://dl.acm.org/doi/10.1007/s10796-021-10231-8
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Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering

New Avenues in Opinion Mining and Sentiment Analysis

Building a Concept-Level Sentiment Dictionary Based on Commonsense Knowledge

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