Multidimensional Author Profling for Social Business Intelligence
Impacto
Scholar |
Otros documentos de la autoría: Lanza Cruz, Indira Lázara; Berlanga Llavori, Rafael; Aramburu Cabo, María José
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadatos
Título
Multidimensional Author Profling for Social Business IntelligenceFecha de publicación
2023Editor
SpringerCita bibliográfica
Lanza-Cruz, I., Berlanga, R. & Aramburu, M.J. Multidimensional Author Profiling for Social Business Intelligence. Inf Syst Front 26, 195–215 (2024). https://doi.org/10.1007/s10796-023-10370-0Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s10796-023-10370-0Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper presents a novel author profling method specially aimed at classifying social network users into the multidimensional perspectives for social business intelligence (SBI) applications. In this scenario, being ... [+]
This paper presents a novel author profling method specially aimed at classifying social network users into the multidimensional perspectives for social business intelligence (SBI) applications. In this scenario, being the user profles defned on
demand for each particular SBI application, we cannot assume the existence of labelled datasets for training purposes. Thus,
we propose an unsupervised method to obtain the required labelled datasets for training the profle classifers. Contrary to
other author profling approaches in the literature, we only make use of the users’ descriptions, which are usually part of
the metadata posts. We exhaustively evaluated the proposed method under four diferent tasks for multidimensional author
profling along with state-of-the-art text classifers. We achieved performances around 88% and 98% of F1 score for a gold
standard and a silver standard datasets respectively. Additionally, we compare our results to other supervised approaches
previously proposed for two of our tasks, getting very close performances despite using an unsupervised method. To the best
of our knowledge, this is the frst method designed to label user profles in an unsupervised way for training profle classifers
with a similar performance to fully supervised ones. [-]
Publicado en
Information Systems Frontiers, 2024, 26.Entidad financiadora
Ministerio de Industria y Comercio | Universitat Jaume I
Código del proyecto o subvención
PDC2021- 121097-I00 | PREDOC/2017/28
Derechos de acceso
© The Author(s) 2023
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- ICC_Articles [424]
- LSI_Articles [362]