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Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes

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Knowledge Engineering and the Semantic Web (KESW 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 468))

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Abstract

This paper presents an automatic term extraction approach for building a vocabulary that is constantly updated. A prepared dictionary is used for sentiment classification into three classes (positive, neutral, negative). In addition, the results of sentiment classification are described and the accuracy of methods based on various weighting schemes is compared. The paper also demonstrates the computational complexity of generating representations for N dynamic documents depending on the weighting scheme used.

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Author information

Authors and Affiliations

  1. The A.P. Ershov Institute of Informatics Systems (IIS), Siberian Branch of the Russian Academy of Sciences, Russia

    Yuliya Rubtsova

Authors
  1. Yuliya Rubtsova
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Editor information

Editors and Affiliations

  1. University of Ulm, Institute of Artificial Intelligence, 89069, Ulm, Germany

    Pavel Klinov

  2. Mechanics and Optics, Saint Petersburg National Research University of Information Technologies, Saint-Petersburg, Russia

    Dmitry Mouromtsev

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© 2014 Springer International Publishing Switzerland

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Rubtsova, Y. (2014). Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes. In: Klinov, P., Mouromtsev, D. (eds) Knowledge Engineering and the Semantic Web. KESW 2014. Communications in Computer and Information Science, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-319-11716-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-11716-4_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11715-7

  • Online ISBN: 978-3-319-11716-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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