Multidimensional Author Profling for Social Business Intelligence
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Other documents of the author: Lanza Cruz, Indira Lázara; Berlanga Llavori, Rafael; Aramburu Cabo, María José
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadata
Title
Multidimensional Author Profling for Social Business IntelligenceDate
2023Publisher
SpringerBibliographic citation
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/s10796-023-10370-0Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s10796-023-10370-0Version
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Abstract
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. [-]
Is part of
Information Systems Frontiers, 2024, 26.Funder Name
Ministerio de Industria y Comercio | Universitat Jaume I
Project code
PDC2021- 121097-I00 | PREDOC/2017/28
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© The Author(s) 2023
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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