Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan, Huan Liu

TL;DR
This paper introduces a novel method to estimate the effects of multi-aspect textual online reviews on business revenue, accounting for hidden confounders and mediating numerical ratings, using advanced machine learning and causal inference techniques.
Contribution
It proposes a new framework for analyzing multi-aspect reviews considering hidden confounders and mediators, improving understanding of review effects on revenue.
Findings
Multi-aspect reviews significantly impact business revenue.
Hidden confounders influence the estimated effects.
Differentiating effects informs better business strategies.
Abstract
Online review systems are the primary means through which many businesses seek to build the brand and spread their messages. Prior research studying the effects of online reviews has been mainly focused on a single numerical cause, e.g., ratings or sentiment scores. We argue that such notions of causes entail three key limitations: they solely consider the effects of single numerical causes and ignore different effects of multiple aspects -- e.g., Food, Service -- embedded in the textual reviews; they assume the absence of hidden confounders in observational studies, e.g., consumers' personal preferences; and they overlook the indirect effects of numerical causes that can potentially cancel out the effect of textual reviews on business revenue. We thereby propose an alternative perspective to this single-cause-based effect estimation of online reviews: in the presence of hidden…
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Taxonomy
TopicsDigital Marketing and Social Media · Advanced Causal Inference Techniques · Recommender Systems and Techniques
Methodstravel james
