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IJMLC 2021 Vol.11(3): 230-235 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.3.1040

Social Media Marketing Experts’ Perceptions Regarding the Capabilities of a Future Artificial Intelligence Software

Adrian Micu, Alexandru Capatina, Angela-Eliza Micu, Marius Geru, and Radu Lixandroiu

Abstract—The increasing interest in Artificial Intelligence’s impact on Social Media Marketing creates huge opportunities for software providers, whose innovative technologies would be broadly implemented by marketers. This article outlines the results of an exploratory research focused on 100 Social Media Marketing experts (digital agencies’ owners, marketers and freelancers) that assessed the forthcoming AI Media software capabilities, based on social media analytics, reflecting audience, image and sentiment analyses. The goal of this paper is to analyze the ranking of twelve capabilities proposed for the future AI Media software, as they were perceived by the respondents included into the research sample.

Index Terms—Artificial intelligence, deep learning, social media marketing, predictive analytics.

iversity of Galati, 47 Domneasca Street, Galati, Romania (e-mail: adrian.micu@ugal.ro, alexandru.capatina@ugal.ro).
A. E. Micu is with Ovidius University of Constanta, 124 Mamaia Boulevard, Constanta, Romania (e-mail: angelaelizamicu@yahoo.com).
Marius Geru and Radu Lixandroiu are with the Transilvania University of Brasov, 29 Eroilor Boulevard, Brasov, Romania (e-mail: marius@thecon.ro, lixi.radu@unitbv.ro).

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Cite: Adrian Micu, Alexandru Capatina, Angela-Eliza Micu, Marius Geru, and Radu Lixandroiu, "Social Media Marketing Experts’ Perceptions Regarding the Capabilities of a Future Artificial Intelligence Software," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 230-235, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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