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Neural Computing and Applications

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23.04.2020 | Original Article

Electronic word-of-mouth effects on studio performance leveraging attention-based model

verfasst von: Yang Liu, Hao Fei, Qingguo Zeng, Bobo Li, Lili Ma, Donghong Ji, Joaquín Ordieres Meré

Erschienen in: Neural Computing and Applications | Ausgabe 23/2020

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Abstract

While existing studies have established the relationship between electronic word-of-mouth (eWOM) and studio performance, limited research has been conducted to demonstrate how the attention-based model applies to the motion picture industry. In this study, examining a review corpus of seven Hollywood studios, we proved that deep learning with the attention mechanism has the best accuracy in both eWOM and stock price movement. We present both a hierarchical two-layer attention network and hierarchical convoluted attention network (HCAN), which quantify the importance of crucial eWOM features in capturing valuable information from audience members’ reviews. Further, comparing the two case studies, we determined that the HCAN model is superior to both machine learning and attention-based models. Our work helps to highlight the business value of the attention-based model and has implications for studio business decisions.

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Titel: Electronic word-of-mouth effects on studio performance leveraging attention-based model
verfasst von: Yang Liu
Hao Fei
Qingguo Zeng
Bobo Li
Lili Ma
Donghong Ji
Joaquín Ordieres Meré
Publikationsdatum: 23.04.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 23/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-04937-0

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