2021/12/19 by Lu Cheng, Cheng Lu, Ruocheng Guo +4
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Expert finding and Q&A systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Recommender Systems and Techniques #Sentiment Analysis and Opinion Mining #cs.AI #cs.LG #stat.ME
paper · pdf · doi:10.48550/arxiv.2112.10274
10 pages, 6 figures, accepted to WSDM22
openalex publication_date 2021/12/19 · arxiv created 2022/01/02 · arxiv updated 2022/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.