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Reducing the Degradation of Sentiment Analysis for Text Collections Spread over a Period of Time

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Knowledge Engineering and Semantic Web (KESW 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 786))

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Abstract

This paper presents approaches to improve sentiment classification in dynamically updated text collections in natural language. As social networks are constantly updated by users there is essential to take into account new jargons, vital discussed topics while solving classification task. Therefore two fundamentally different methods for solution this problem are suggested. Supervised machine learning method and unsupervised machine learning method are used for sentiment analysis. The methods are compared and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described.

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Authors and Affiliations

  1. A.P. Ershov Institute of Informatics Systems, Novosibirsk State Universiry, Novosibirsk, Russia

    Yuliya Rubtsova

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  1. Yuliya Rubtsova
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Correspondence to Yuliya Rubtsova .

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Editors and Affiliations

  1. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Przemysław Różewski

  2. University of Bonn, Bonn, Germany

    Christoph Lange

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Rubtsova, Y. (2017). Reducing the Degradation of Sentiment Analysis for Text Collections Spread over a Period of Time. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-69548-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69547-1

  • Online ISBN: 978-3-319-69548-8

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