将用户评论转为带情感的图谱，预测评分更准且可解释。
ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features
- 从评论中提取主谓宾三元组并加情感分，构建知识图谱
- 在HotelRec数据集上性能接近大模型，但计算成本更低
- 图结构提升可解释性，适合想看分析逻辑的研究者
在酒店业中，理解影响客户评分的因素对提升满意度和业务表现至关重要。本文提出ReviewGraph框架，通过提取（主体，谓词，客体）三元组并关联情感得分，将文本评论转化为知识图谱，结合图嵌入（Node2Vec）与情感特征，使用机器学习分类器预测评分。在HotelRec数据集上，对比传统NLP基线（如词袋、TF-IDF、Word2Vec）和大语言模型（LLMs），ReviewGraph性能接近现有最优模型，但无需集成即可实现更低计算开销。尽管在准确率上与大模型相当，但在基于一致性的评估指标（如Cohen's Kappa）上表现更优，且具备更强可解释性、可视化探索能力，并可集成至检索增强生成（RAG）系统中。该工作展示了图表示在评论分析中的潜力，为未来融合先进图神经网络与微调大模型抽取方法的研究奠定基础。相关输出与平台将在GitHub公开：https://github.com/aaronlifenghan/ReviewGraph
原文摘要 · Abstract (English)
In the hospitality industry, understanding the factors that drive customer review ratings is critical for improving guest satisfaction and business performance. This work proposes ReviewGraph for Review Rating Prediction (RRP), a novel framework that transforms textual customer reviews into knowledge graphs by extracting (subject, predicate, object) triples and associating sentiment scores. Using graph embeddings (Node2Vec) and sentiment features, the framework predicts review rating scores through machine learning classifiers. We compare ReviewGraph performance with traditional NLP baselines (such as Bag of Words, TF-IDF, and Word2Vec) and large language models (LLMs), evaluating them in the HotelRec dataset. In comparison to the state of the art literature, our proposed model performs similar to their best performing model but with lower computational cost (without ensemble). While ReviewGraph achieves comparable predictive performance to LLMs and outperforms baselines on agreement-based metrics such as Cohen's Kappa, it offers additional advantages in interpretability, visual exploration, and potential integration into Retrieval-Augmented Generation (RAG) systems. This work highlights the potential of graph-based representations for enhancing review analytics and lays the groundwork for future research integrating advanced graph neural networks and fine-tuned LLM-based extraction methods. We will share ReviewGraph output and platform open-sourced on our GitHub page https://github.com/aaronlifenghan/ReviewGraph
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