XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoning

TL;DR

This paper proposes XCom, an enhanced transformer-based model separated into two principal modules, i.e., aspect-based rating prediction and semantic analysis for comparative opinion mining, which achieves leading performances compared to other baselines and demonstrates its effectiveness in providing meaningful explanations.

Abstract

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.

Figures (6)

• Figure 1: Illustration of explicit vs. implicit comparative opinions in user reviews. Explicit: Direct comparison using terms like "is richer in taste than". Implicit: Two separate reviews are from the same user. Preference is implied, and not stated, which is harder to detect. Insufficient information in the cross-user setting: Two reviews are from different users. Comparisons are unreliable due to subjective differences in user standards (e.g., leniency, writing style), lacking a common baseline for inference.
• Figure 2: XCom architecture.
• Figure 3: Distribution of aspect and comparative labels.
• Figure 4: The SHAP-based explanation examples for taste and appearance aspects. In our SHAP-based visualization, red tokens indicate a positive impact to the model's output, blue tokens indicate negative impact. The arrow length represents the absolute SHAP value. The following highlighted text provides a more intuitive explanation by marking key tokens in the original text. More intense colors signify stronger influence.
• Figure 5: SHAP faithfulness evaluation: Macro-F1 drop when removing top-k vs. bottom-k adjectives.