A self-explanatory contrastive logical knowledge learning method for sentiment analysis

Yulin Chen, Bo Yuan, Beishui Liao, Dov M. Gabbay

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Although interpretable methods for deep learning models have become popular in sentiment analysis domains in recent years, existing methods still face the challenge of providing predictions with both high accuracy and user-friendly explanations. To address this problem, we propose a novel framework called Contrasting Logical Knowledge Learning (CLK) that utilizes contrastive learning, label knowledge, and logical rule learning. Logical rule learning is used to provide human-understandable explanations while label knowledge and contrastive learning are used to achieve high performance on both pre-trained models and ordinary DNNs. To ensure model interpretability, we design a novel knowledge reasoning strategy based on learned logical rules and trained models. Empirical results from binary sentiment analysis tasks and fine-grained sentiment analysis tasks show that CLK can effectively balance accuracy and interpretability. Additionally, we conduct two case studies to demonstrate the process of explanation generation and knowledge reasoning, which shows that our method's explanations are causally consistent with the implicit model decision logic.

Original languageEnglish
Article number110863
JournalKnowledge-Based Systems
Volume278
DOIs
StatePublished - 25 Oct 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Funding

The research reported in this paper was partially supported by the National Key Research and Development Program of China ( 2022YFC3340900 ), the “2030 Megaproject” - New Generation Artificial Intelligence of China under Grant No. 2018AAA0100904 , and the National Social Science Fund of China ( 20 & ZD047 ).

FundersFunder number
New Generation Artificial Intelligence of China2018AAA0100904
National Key Research and Development Program of China2022YFC3340900
National Office for Philosophy and Social SciencesZD047

    Keywords

    • Contrastive learning
    • First order logic
    • Interpretable sentiment analysis
    • Knowledge reasoning

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