A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making
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Título: | A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making |
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Autor/es: | Kauffmann, Erick | Peral, Jesús | Gil, David | Ferrández, Antonio | Sellers Rubio, Ricardo | Mora, Higinio |
Grupo/s de investigación o GITE: | Lucentia | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) | Marketing | Arquitecturas Inteligentes Aplicadas (AIA) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Marketing |
Palabras clave: | Big data analytics | Sentiment analysis | Marketing decisions | High-tech industries | Fake reviews |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos | Arquitectura y Tecnología de Computadores | Comercialización e Investigación de Mercados |
Fecha de publicación: | oct-2020 |
Editor: | Elsevier |
Cita bibliográfica: | Industrial Marketing Management. 2020, 90: 523-537. https://doi.org/10.1016/j.indmarman.2019.08.003 |
Resumen: | User-generated content about brands is an important source of big data that can be transformed into valuable information. A huge number of items are reviewed and rated by consumers on a daily basis, and managers have a keen interest in real-time monitoring of this information to improve decision-making. The main challenge is to mine reliable textual consumer opinions, and automatically use them to rate the best products or brands. We propose a framework to automatically analyse these reviews, transforming negative and positive user opinions in a quantitative score. Sentiment analysis was employed to analyse online reviews on Amazon. The Fake Review Detection Framework—FRDF— detects and removes fake reviews using Natural Language Processing technology. The FRDF was tested on reviews of products from high-tech industries. Brands were rated according to consumer sentiment. The findings demonstrate that brand managers and consumers would find this tool useful, in combination with the 5-Star score, for more comprehensive decision-making. For instance, the FRDF ranks the best products by price alongside their respective sentiment value and the 5-Star score. |
Patrocinador/es: | This work was supported in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) through the project CloudDriver4Industry under Grant TIN2017-89266-R, in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32, and in part by the Conselleria de Educación, Investigación, Cultura y Deporte of the Community of Valencia, Spain, within the Program of Support for Research under Project AICO/2017/134 and Project PROMETEO/2018/089. |
URI: | http://hdl.handle.net/10045/109819 |
ISSN: | 0019-8501 (Print) | 1873-2062 (Online) |
DOI: | 10.1016/j.indmarman.2019.08.003 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 Elsevier Inc. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.indmarman.2019.08.003 |
Aparece en las colecciones: | INV - GPLSI - Artículos de Revistas INV - MKT - Artículos de Revistas INV - LUCENTIA - Artículos de Revistas INV - AIA - Artículos de Revistas |
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Kauffmann_etal_2020_IndustrialMarketingManag_final.pdf | Versión final (acceso restringido) | 954,54 kB | Adobe PDF | Abrir Solicitar una copia |
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