XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoning
This work addresses the problem of trust in AI systems for users by making comparative opinion mining more interpretable, though it is incremental as it builds on existing transformer and XAI methods.
The paper tackles the lack of transparency in transformer-based models for comparative opinion mining by proposing XCom, which integrates aspect-based rating prediction and semantic analysis with Shapley additive explanations, achieving leading performance compared to baselines.
Comparative opinion mining involves comparing products from different reviews. However, transformer-based models designed for this task often lack transparency, which can adversely hinder the development of trust in users. In this paper, we propose XCom, an enhanced transformer-based model separated into two principal modules, i.e., (i) aspect-based rating prediction and (ii) semantic analysis for comparative opinion mining. XCom also incorporates a Shapley additive explanations module to provide interpretable insights into the model's deliberative decisions. Empirically, XCom achieves leading performances compared to other baselines, which demonstrates its effectiveness in providing meaningful explanations, making it a more reliable tool for comparative opinion mining. Source code is available at: https://anonymous.4open.science/r/XCom.