用文本因果分析拆解在线评分影响因素，更准判断哪些方面最重要。
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
- 基于CausalBERT改进方法，通过文本提及识别影响评分的真实因素。
- 在60万条美国中小学评价数据上验证，管理与考试表现是评分关键驱动。
- 提升估计可靠性，适合想挖掘用户评价深层原因的研究者使用。
在线评论为产品或服务各方面的感知质量提供了宝贵见解。尽管基于方面的情感分析已能从评论中提取方面，但较少研究关注每个方面对整体评价的影响。这尤其困难，因为各方面之间存在相关性，难以分离各自效应。本文引入一种基于文本因果分析的新方法，特别是CausalBERT，以解耦各因素对总体评分的影响。我们对CausalBERT进行了三项关键改进：温度缩放以获得更校准的处理分配估计；超参数优化以减少混杂变量过度调整；以及可解释性方法来刻画发现的混杂因素。本研究将评论中的文本提及视为现实属性的代理。我们在超过60万条美国K-12学校评论的真实与半合成数据上验证了该方法。结果表明，所提改进使估计更可靠，且学校管理与基准测试表现是整体评分的重要驱动因素。
原文摘要 · Abstract (English)
Online reviews provide valuable insights into the perceived quality of facets of a product or service. While aspect-based sentiment analysis has focused on extracting these facets from reviews, there is less work understanding the impact of each aspect on overall perception. This is particularly challenging given correlations among aspects, making it difficult to isolate the effects of each. This paper introduces a methodology based on recent advances in text-based causal analysis, specifically CausalBERT, to disentangle the effect of each factor on overall review ratings. We enhance CausalBERT with three key improvements: temperature scaling for better calibrated treatment assignment estimates; hyperparameter optimization to reduce confound overadjustment; and interpretability methods to characterize discovered confounds. In this work, we treat the textual mentions in reviews as proxies for real-world attributes. We validate our approach on real and semi-synthetic data from over 600K reviews of U.S. K-12 schools. We find that the proposed enhancements result in more reliable estimates, and that perception of school administration and performance on benchmarks are significant drivers of overall school ratings.
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