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Aspect Terms Extraction of Arabic Dialects for Opinion Mining Using Conditional Random Fields

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

Abstract

While English opinion mining has been studied extensively, Arabic fine grained opinion mining has not received much attention. This paper looks at employing conditional random fields as a supervised method to extract aspect terms which can then be employed for fine grained opinion mining. Despite the lack of Arabic Dialect NLP tools that limited the amount of improvement that can be added to the algorithm, Our analysis shows a comparable level of precision and recall to what has been achieved for English.

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Authors and Affiliations

  1. University of Pittsbrugh, Pittsburgh, USA

    Alawya Alawami

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  1. Alawya Alawami
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Correspondence to Alawya Alawami .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Alawami, A. (2018). Aspect Terms Extraction of Arabic Dialects for Opinion Mining Using Conditional Random Fields. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-75487-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75486-4

  • Online ISBN: 978-3-319-75487-1

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