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

Xianbin Hong, Sheng Uei Guan*, Nian Xue, Zhen Li*, Ka Lok Man, Prudence W.H. Wong, Dawei Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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 (Formula presented.) promotion of the Macro-F1 score on the Twitter sentiment classification task and a (Formula presented.) promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively.

Original languageEnglish
Article number1241
JournalApplied Sciences (Switzerland)
Issue number3
Publication statusPublished - Feb 2023


  • expert system
  • fine-grained sentiment classification
  • knowledge graph
  • lifelong machine learning
  • reinforcement learning


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