Semantic Feature Aggregation for Gender Identification in Russian Facebook

Ch. 1. P. 3–15.

Panicheva P., Mirzagitova A., Ledovaya Y.

*Реализация соц. сети Facebook запрещена на территории России по основаниям осуществления экстремистской деятельности.

The goal of the current work is to evaluate semantic feature aggregation techniques in a task of gender classification of public social media texts in Russian. We collect Facebook posts of Russian-speaking users and apply them as a dataset for two topic modelling techniques and a distributional clustering approach. The output of the algorithms is applied as a feature aggregation method in a task of gender classification based on a smaller Facebook sample. The classification performance of the best model is favorably compared against the lemmas baseline and the state-of-the-art results reported for a different genre or language. The resulting successful features are exemplified, and the difference between the three techniques in terms of classification performance and feature contents are discussed, with the best technique clearly outperforming the others.

Keywords: distributional semantics, тематическое моделирование, topic modelling, дистрибутивная семантика, author profiling, профилирование автора

In book

Artificial Intelligence and Natural Language, 6th Conference, AINL 2017, St. Petersburg, Russia, September 20–23, 2017, Revised Selected Papers

Issue 789. , Switzerland: Springer, 2018.