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Emotion Classification in Russian: Feature Engineering and Analysis

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Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12602))

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Abstract

In this paper, we address the issue of identifying emotions in Russian informal text messages. For this purpose, a new large dataset of text messages from the most popular Russian messaging/social networking services (Telegram, VK) was compiled semi-automatically. Emojis contained in the text messages were used to annotate the data for emotions expressed. This paper proposes an integrated approach to text-based emotion classification combining linguistic methods and machine learning. This approach relies on morphological, lexical, and stylistic features of the text. Furthermore, the level of expressiveness was considered as well. As a result, an emotion classification model demonstrating near-human performance was designed. In this paper, we also report on the importance of different linguistic features of the text messages for the task of automatic emotive analysis. Additionally, we perform error analysis and discover ways to improve the model in the future.

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Notes

  1. 1.

    By emotives we mean any language units, not only lexis, that are used to express emotions.

  2. 2.

    One with manually tagged messages, another with 50K messages marked up semi-automatically; for more details, please refer to Sect. 3.

  3. 3.

    https://vk.com.

  4. 4.

    https://telegram.org.

  5. 5.

    https://github.com/asbabiy/AIST/blob/master/Parser.py.

  6. 6.

    https://github.com/IlyaGusev/rnnmorph.

  7. 7.

    https://github.com/asbabiy/AIST/blob/master/msg_with_emoji.csv.

  8. 8.

    https://github.com/asbabiy/AIST/blob/master/train_data.csv.

  9. 9.

    https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html.

  10. 10.

    https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html.

  11. 11.

    https://eli5.readthedocs.io/en/latest/.

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Acknowledgments

The work is supported by RSF (Russian Science Foundation) grant 20-71-10010.

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Marina Kazyulina, Aleksandr Babii & Alexey Malafeev

Authors
  1. Marina Kazyulina
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  2. Aleksandr Babii
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  3. Alexey Malafeev
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Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre lès Nancy, France

    Amedeo Napoli

  11. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  12. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  13. Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  14. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Kazyulina, M., Babii, A., Malafeev, A. (2021). Emotion Classification in Russian: Feature Engineering and Analysis. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-72610-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72609-6

  • Online ISBN: 978-3-030-72610-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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