dc.creator | Aguilar Moreno, Juan Antonio | es |
dc.creator | Palos Sánchez, Pedro Ramiro | es |
dc.creator | Pozo Barajas, Rafael del | es |
dc.date.accessioned | 2024-03-08T08:02:48Z | |
dc.date.available | 2024-03-08T08:02:48Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Aguilar Moreno, J.A., Palos Sánchez, P.R. y Pozo Barajas, R.d. (2024). Sentiment analysis to support business decision-making. A bibliometric study. AIMS Mathematics, 9 (2), 4337-4375. https://doi.org/10.3934/math.2024215. | |
dc.identifier.issn | 2473-6988 | es |
dc.identifier.uri | https://hdl.handle.net/11441/155960 | |
dc.description.abstract | Customer feedback on online platforms is an unstructured database of growing importance
for organizations, which together with the rise of Natural Language Processing algorithms is
increasingly present when making decisions. In this paper, a bibliometric analysis is carried out with
the intention of understanding the prevailing state of research about the adoption of sentiment analysis
methods in organizations when making decisions . It is also a goal to comprehend which business
sectors and areas within the company they are most applied and to identify what future challenges
that in this area may arise , as well a s the main topics, authors, articles, countries and universities most
influential in the scientific literature. To this end, a total of 101 articles have been gathered from the
Scopus and Clarivate Analytics Web of Science (WoS ) databases, of which 85 were selected for
analysis using the Bibliometrix tool. This study highlights the growing popularity of sentiment analysis
methods combined with Multicriteria Decision Making and predictive algorithms. Twitter and Amazon
are commonly used data sources, with applications across multiple sectors (supply chain, financial, etc.).
Sentiment a nalysis enhances decision making and promotes customer centric approaches. | es |
dc.format | application/pdf | es |
dc.format.extent | 39 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springfield, MO : AIMS Press | es |
dc.relation.ispartof | AIMS Mathematics, 9 (2), 4337-4375. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Social media | es |
dc.subject | Big data | es |
dc.subject | Business decision-making | es |
dc.subject | Data mining | es |
dc.subject | Opinion mining | es |
dc.subject | Sentiment mining | es |
dc.subject | Bibliometric analysis | es |
dc.subject | Multicriteria decision making | es |
dc.title | Sentiment analysis to support business decision-making. A bibliometric study | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Contabilidad y Economía Financiera | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Economía Financiera y Dirección de Operaciones | es |
dc.relation.publisherversion | http://www.aimspress.com/article/doi/10.3934/math.2024215 | es |
dc.identifier.doi | 10.3934/math.2024215 | es |
dc.journaltitle | AIMS Mathematics | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | 4337 | es |
dc.publication.endPage | 4375 | es |