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Semantic Feature Aggregation for Gender Identification in Russian Facebook

  • Polina PanichevaEmail author
  • Aliia Mirzagitova
  • Yanina Ledovaya
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 789)

Abstract

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.

Notes

Acknowledgments

The authors acknowledge Saint-Petersburg State University for a research grant 8.38.351.2015. The reported study is also supported by RFBR grant 16-06-00529.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Polina Panicheva
    • 1
    Email author
  • Aliia Mirzagitova
    • 1
  • Yanina Ledovaya
    • 1
  1. 1.St. Petersburg State UniversitySt. PetersburgRussia

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