XCom通过可解释性增强，提升产品对比评论分析的透明度与可靠性。
XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoning
- 分两模块：基于方面评分预测与语义分析进行对比评论挖掘。
- 引入Shapley解释模块，实现决策过程可解释。
- 在多个数据集上表现领先，适合需要可信分析的场景。
对比意见挖掘需从不同评论中比较产品优劣，但现有基于Transformer的模型缺乏透明性，影响用户信任。本文提出XCom，一种分两模块的增强型Transformer模型：(i) 基于方面的评分预测，(ii) 用于对比意见挖掘的语义分析。此外，XCom集成Shapley加法解释模块，提供模型决策的可解释性。实证结果表明，相比其他基线模型，XCom在多个数据集上表现领先，验证了其在生成有意义解释方面的有效性，使其成为更可靠的对比意见挖掘工具。源代码已公开：https://anonymous.4open.science/r/XCom。
原文摘要 · Abstract (English)
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.
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