Evaluation and Sociolinguistic Analysis of Text Features for Gender and Age Identification
- 1 Lund University, Sweden
- 2 University of Hertfordshire, United Kingdom
- 3 University of Patras, Greece
Copyright: © 2020 Vasiliki Simaki, Iosif Mporas and Vasileios Megalooikonomou. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The paper presents an interdisciplinary study in the field of automatic gender and age identification, under the scope of sociolinguistic knowledge on gendered and age linguistic choices that social media users make. The authors investigated and gathered standard and novel text features used in text mining approaches on the author's demographic information and profiling and they examined their efficacy in gender and age detection tasks on a corpus consisted of social media texts. An analysis of the most informative features is attempted according to the nature of each feature and the information derived after the characteristics' score of importance is discussed.
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- Text Mining
- Feature Ranking
- ReliefF Algorithm
- Gender Detection
- Age Identification