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Semantic Feature Aggregation for Gender Identification in Russian Facebook

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Artificial Intelligence and Natural Language (AINL 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 789))

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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.

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Notes

  1. 1.

    https://yandex.ru/.

  2. 2.

    http://wwbp.org.

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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.

Author information

Authors and Affiliations

  1. St. Petersburg State University, Universitetskaya nab. 7-9, 199034, St. Petersburg, Russia

    Polina Panicheva, Aliia Mirzagitova & Yanina Ledovaya

Authors
  1. Polina Panicheva
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  2. Aliia Mirzagitova
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  3. Yanina Ledovaya
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Corresponding author

Correspondence to Polina Panicheva .

Editor information

Editors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  2. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

  3. Mendel University , Brno, Czech Republic

    Jan Žižka

Appendix

Appendix

Table 6. Significant lemmas (English translation)
Full size table
Table 7. Significant LDA topics (English translation)
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Table 8. Significant clusters (English translation)
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Table 9. Significant ATM topics (English translation)
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Panicheva, P., Mirzagitova, A., Ledovaya, Y. (2018). Semantic Feature Aggregation for Gender Identification in Russian Facebook. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-71746-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-71746-3_1

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