Estimating causal effects of multi-aspect online reviews with multi-modal proxies

Lu Cheng, Ruocheng Guo, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Online reviews enable consumers to engage with companies and provide important feedback. Due to the complexity of the high-dimensional text, these reviews are often simplified as a single numerical score, e.g., ratings or sentiment scores. This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e.g., the Food and Service of a restaurant. Understanding consumers' opinions toward different aspects can help evaluate business performance in detail and strategize business operations effectively. Specifically, we aim to answer interventional questions such as What will the restaurant popularity be if the quality w.r.t. its aspect Service is increased by 10%? The defining challenge of causal inference with observational data is the presence of "confounder", which might not be observed or measured, e.g., consumers' preference to food type, rendering the estimated effects biased and high-variance. To address this challenge, we have recourse to the multi-modal proxies such as the consumer profile information and interactions between consumers and businesses. We show how to effectively leverage the rich information to identify and estimate causal effects of multiple aspects embedded in online reviews. Empirical evaluations on synthetic and real-world data corroborate the efficacy and shed light on the actionable insight of the proposed approach.

Original languageEnglish (US)
Title of host publicationWSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages103-112
Number of pages10
ISBN (Electronic)9781450391320
DOIs
StatePublished - Feb 11 2022
Event15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual, Online, United States
Duration: Feb 21 2022Feb 25 2022

Publication series

NameWSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining

Conference

Conference15th ACM International Conference on Web Search and Data Mining, WSDM 2022
Country/TerritoryUnited States
CityVirtual, Online
Period2/21/222/25/22

Keywords

  • Causal effect estimation
  • Hidden confounder
  • Multi-aspect sentiment
  • Multi-modality
  • Online reviews

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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