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Recursive Neural Text Classification Using Discourse Tree Structure for Argumentation Mining and Sentiment Analysis Tasks

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Foundations of Intelligent Systems (ISMIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12117))

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Abstract

This paper considers sentiment classification of movie reviews and two argument mining tasks: verification of political statements and categorization of quotes from an Internet forum corresponding to argumentation (factual or emotional). In the case of the fact-checking problem, justifications can be used additionally in one of its sub-tasks. A strong model for solving these and similar problems still does not exist. It requires the style-based approach to achieve the best results. The proposed model effectively encodes parsed discourse trees due to the recursive neural network. The novel siamese model based on it is suggested to analyze discourse structures for the pairs of texts. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that the proposed models are effective and reach the best results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the classification of longer texts.

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Acknowledgments

The article was prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5-100’.

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Authors and Affiliations

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

    Alexander Chernyavskiy & Dmitry Ilvovsky

Authors
  1. Alexander Chernyavskiy
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  2. Dmitry Ilvovsky
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Corresponding author

Correspondence to Alexander Chernyavskiy .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Denis Helic

  2. University of Klagenfurt, Klagenfurt, Austria

    Gerhard Leitner

  3. Graz University of Technology, Graz, Austria

    Martin Stettinger

  4. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  5. University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Raś

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Chernyavskiy, A., Ilvovsky, D. (2020). Recursive Neural Text Classification Using Discourse Tree Structure for Argumentation Mining and Sentiment Analysis Tasks. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-59491-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

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