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    <abstract>The success of state-of-the-art Natural Language Processing (NLP) systems heavily depends on deep neural networks, which excel in various tasks through strong data fitting and latent feature modeling abilities. However, certain challenges linked to deep neural networks and supervised deep learning deserve considerations, e.g., extensive computing resources, knowledge forgetting, etc. Previous research attempted to tackle these challenges individually through irrelative techniques. However, they do not instigate fundamental shifts in the learning paradigm. In this work, we propose a novel neurosymbolic method for sentiment analysis to tackle these issues. We also propose a novel sentiment-pragmatic knowledge base that places emphasis on human subjectivity within varying domain annotations. We conducted extensive experiments to show that our neurosymbolic framework for sentiment analysis stands out for its lightweight nature, robustness across domains and languages, efficient few-shot training, and rapid convergence.</abstract>
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%0 Conference Proceedings
%T SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis
%A Zhang, Xulang
%A Mao, Rui
%A Cambria, Erik
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F zhang-etal-2024-senticvec
%X The success of state-of-the-art Natural Language Processing (NLP) systems heavily depends on deep neural networks, which excel in various tasks through strong data fitting and latent feature modeling abilities. However, certain challenges linked to deep neural networks and supervised deep learning deserve considerations, e.g., extensive computing resources, knowledge forgetting, etc. Previous research attempted to tackle these challenges individually through irrelative techniques. However, they do not instigate fundamental shifts in the learning paradigm. In this work, we propose a novel neurosymbolic method for sentiment analysis to tackle these issues. We also propose a novel sentiment-pragmatic knowledge base that places emphasis on human subjectivity within varying domain annotations. We conducted extensive experiments to show that our neurosymbolic framework for sentiment analysis stands out for its lightweight nature, robustness across domains and languages, efficient few-shot training, and rapid convergence.
%U https://aclanthology.org/2024.findings-acl.289
%P 4851-4863

Markdown (Informal)

[SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis](https://aclanthology.org/2024.findings-acl.289) (Zhang et al., Findings 2024)

• SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis (Zhang et al., Findings 2024)

ACL

• Xulang Zhang, Rui Mao, and Erik Cambria. 2024. SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis. In Findings of the Association for Computational Linguistics ACL 2024, pages 4851–4863, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.