Title | ||
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A comparative analysis of distributional term representations for author profiling in social media. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2019 | 10.3233/JIFS-179033 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | DocType | Volume |
Author profiling,document representation,distributional term representation,text classification,social media | Journal | 36 |
Issue | ISSN | Citations |
SP5 | 1064-1246 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Miguel Ángel Álvarez Carmona | 1 | 1 | 0.69 |
Esaú Villatoro-Tello | 2 | 3 | 2.15 |
Manuel Montes-Y-Gómez | 3 | 638 | 83.97 |
Luis Villaseñor-Pineda | 4 | 403 | 53.74 |