Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales

MIS Quarterly Forthcoming

Posted: 18 Dec 2019 Last revised: 20 May 2022

See all articles by Yifan Yu

Yifan Yu

University of Washington - Michael G. Foster School of Business; Amazon

Yang Yang

School of Management, University of Science and Technology of China

Jinghua Huang

Tsinghua University - Department of Management Science and Engineering

Yong Tan

University of Washington - Michael G. Foster School of Business

Date Written: December 3, 2019

Abstract

Emotion artificial intelligence, the algorithm that recognizes and interprets various human emotions beyond valence (positive and negative polarity), is still in its infancy yet attracts much attention from both the industry and the academia. Based on the discrete emotion theory and statistical language modeling, this work proposes an algorithm to enable automatic domain-adaptive emotion lexicon construction and multi-dimensional emotion detection in texts. With a large-scale dataset of China’s movie market from 2012 to 2018, we construct and validate a domain-specific emotion lexicon and demonstrate the predictive power of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) in online reviews on box office. Further, we find that representing emotions using discrete emotions yields higher prediction accuracy than using valence or latent emotion variables generated by topic modeling. To understand the source of the predictive power from a theoretical perspective, and to test the cross-culture generalizability of our prediction study, we further conduct an experiment in the U.S. movie market, based on the “feelings as information” theory and theoretical research on emotion, judgement, and decision making. We find that mediated by processing fluency, discrete emotions significantly affect the perceived review helpfulness, which further influences purchase intention. Our work shows the economic value of emotions in online reviews, generates insight into the mechanism of their effects, and has managerial implications for online review platform design, movie marketing, and cinema operations.

Keywords: emotions, online reviews, box office, Word2Vec, processing fluency, perceived helpfulness, purchase intention

Suggested Citation

Yu, Yifan and Yang, Yang and Huang, Jinghua and Tan, Yong, Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales (December 3, 2019). MIS Quarterly Forthcoming, Available at SSRN: https://ssrn.com/abstract=3497884 or http://dx.doi.org/10.2139/ssrn.3497884

Yifan Yu

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

HOME PAGE: http://staff.washington.edu/yifanyu/pro/

Amazon ( email )

Yang Yang

School of Management, University of Science and Technology of China ( email )

Hefei, Anhui
China

Jinghua Huang (Contact Author)

Tsinghua University - Department of Management Science and Engineering ( email )

United States

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

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