Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication

Authors

  • Lu Cheng Arizona State University
  • Ruocheng Guo City University of Hong Kong
  • Kasim Candan Arizona State University
  • Huan Liu Arizona State University

Keywords:

Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Trust; reputation; recommendation systems, Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health

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 confounders, we consider multi-aspect textual reviews, particularly, their total effects on business revenue and direct effects with the numerical cause -- ratings -- being the mediator. We draw on recent advances in machine learning and causal inference to together estimate the hidden confounders and causal effects. We present empirical evaluations using real-world examples to discuss the importance and implications of differentiating the multi-aspect effects in strategizing business operations.

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Published

2022-05-31

How to Cite

Cheng, L., Guo, R., Candan, K., & Liu, H. (2022). Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 67-78. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19273