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Aspect Extraction from Reviews Using Conditional Random Fields

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Knowledge Engineering and Semantic Web (KESW 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 518))

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Abstract

This paper describes an information extraction and content analysis system. The proposed system is based on a conditional random field algorithm and intended to extract aspect terms mentioned in the text. We use a set of morphological features for machine learning. The system is used for automatic extraction of explicit aspects and also to automatic extraction of all aspects (explicit, implicit and sentiment facts), and tested on two domains: restaurants and automobiles. We show that our system can produce quite a high level of precision which means that the system is capable of recognizing aspect terms rather accurately. The system demonstrates that even a small set of features for conditional random field algorithm can perform competitively and shows good results.

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Author information

Authors and Affiliations

  1. A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia

    Yuliya Rubtsova

  2. Independent Developer, Novokuznetsk, Russia

    Sergey Koshelnikov

Authors
  1. Yuliya Rubtsova
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  2. Sergey Koshelnikov
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Corresponding author

Correspondence to Yuliya Rubtsova .

Editor information

Editors and Affiliations

  1. Complexible Inc, Washington, District of Columbia, USA

    Pavel Klinov

  2. ITMO University, St. Petersburg, Russia

    Dmitry Mouromtsev

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Rubtsova, Y., Koshelnikov, S. (2015). Aspect Extraction from Reviews Using Conditional Random Fields. In: Klinov, P., Mouromtsev, D. (eds) Knowledge Engineering and Semantic Web. KESW 2015. Communications in Computer and Information Science, vol 518. Springer, Cham. https://doi.org/10.1007/978-3-319-24543-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-24543-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24542-3

  • Online ISBN: 978-3-319-24543-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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