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Analysis of institutional authors

Ordieres Meré J.Author

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May 12, 2020
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Electronic word-of-mouth effects on studio performance leveraging attention-based model

Publicated to: NEURAL COMPUTING & APPLICATIONS. 32 (23): 17601-17622 - 2020-12-01 32(23), DOI: 10.1007/s00521-020-04937-0

Authors:

Liu, Yang; Fei, Hao; Zeng, Qingguo; Li, Bobo; Ma, Lili; Ji, Donghong; Ordieres Mere, Joaquin
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Affiliations

South China Normal Univ, Sch Math Sci, Guangzhou 510631, Peoples R China - Author
South China Normal University - Author
Univ Politecn Madrid, Dept Ind Engn Business Adm & Stat, Escuela Tecn Super Ingenieros Ind, E-28006 Madrid, Spain - Author
Universidad Politécnica de Madrid - Author
Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China - Author
Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China - Author
Wuhan University - Author
Zhongnan Univ Econ & Law, Wenlan Sch Business, Wuhan 430073, Peoples R China - Author
Zhongnan University of EcoNomics and Law - Author
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Abstract

© 2020, Springer-Verlag London Ltd., part of Springer Nature. 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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Keywords

Attention mechanismAudience reviewDeep learningElectronic word-of-mouthStock market

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal NEURAL COMPUTING & APPLICATIONS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position 31/139, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-09:

  • Google Scholar: 20
  • WoS: 14
  • Scopus: 17
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-09:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 41.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 40 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/62512/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 364
  • Downloads: 419
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Liu Y) and Last Author (ORDIERES MERE, JOAQUIN BIENVENIDO).

the author responsible for correspondence tasks has been Liu Y.

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