Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis

Hong, Xianbin ORCID: 0000-0003-1678-0948, Guan, Sheng-Uei, Xue, Nian, Li, Zhen, Man, Ka Lok, Wong, Prudence WH ORCID: 0000-0001-7935-7245 and Liu, Dawei (2023) Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis. APPLIED SCIENCES-BASEL, 13 (3). p. 1241.

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Official URL: http://dx.doi.org/10.3390/app13031241

Abstract

<jats:p>Artificial intelligence (AI) systems are becoming wiser, even surpassing human performances in some fields, such as image classification, chess, and Go. However, most high-performance AI systems, such as deep learning models, are black boxes (i.e., only system inputs and outputs are visible, but the internal mechanisms are unknown) and, thus, are notably challenging to understand. Thereby a system with better explainability is needed to help humans understand AI. This paper proposes a dual-track AI approach that uses reinforcement learning to supplement fine-grained deep learning-based sentiment classification. Through lifelong machine learning, the dual-track approach can gradually become wiser and realize high performance (while keeping outstanding explainability). The extensive experimental results show that the proposed dual-track approach can provide reasonable fine-grained sentiment analyses to product reviews and remarkably achieve a 133% promotion of the Macro-F1 score on the Twitter sentiment classification task and a 27.12% promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	lifelong machine learning, fine-grained sentiment classification, reinforcement learning, expert system, knowledge graph
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	21 Apr 2023 12:54
Last Modified:	15 Mar 2024 04:09
DOI:	10.3390/app13031241
Open Access URL:	https://doi.org/10.3390/app13031241
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169846