| Autor: |
Álvarez-Carmona, Miguel Á., Villatoro-Tello, Esaú, Montes-y-Gómez, Manuel, Villaseñor-Pienda, Luis |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4857-4868, 2019 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.3233/JIFS-179033 |
| Popis: |
Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability. |
| Databáze: |
arXiv |
| Externí odkaz: |
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