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

arXiv: https://arxiv.org/abs/2603.01212 · HTML: https://mlsanity.com/paper/2603.01212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [Better Explain Transformers by Illuminating Important Information](https://mlsanity.com/paper/2401.09972/) (2024)
- [Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives](https://mlsanity.com/paper/2405.05562/) (2024)
- [Comparative Opinion Mining: A Review](https://mlsanity.com/paper/1712.08941/) (2017)
- [Overview of the VLSP 2023 -- ComOM Shared Task: A Data Challenge for Comparative Opinion Mining from Vietnamese Product Reviews](https://mlsanity.com/paper/2402.13613/) (2024)
- [Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews](https://mlsanity.com/paper/2307.03691/) (2023)
- [Comparing Without Saying: A Dataset and Benchmark for Implicit Comparative Opinion Mining from Same-User Reviews](https://mlsanity.com/paper/2601.13575/) (2026)

## Built on by (most cited first, 1 of 1)

- [Privacy Preserving Topic-wise Sentiment Analysis of the Iran Israel USA Conflict Using Federated Transformer Models](https://mlsanity.com/paper/2603.13655/) (2026)

## Abstract (arXiv)

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.