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Mining Popular Menu Items of a Restaurant from Web Reviews

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Book cover Web Information Systems and Mining (WISM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6988))

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Abstract

We propose a novel method to mine popular menu items from online reviews. In order to extract popular menu items, a crawler that uses the wrapper on search web sites was used to collect online reviews, restaurant names, and menu items. Then, unnecessary posts were removed by using the patterns. Also, post frequency was used to find the most frequently appearing menu items from online reviews in order to select the most popular menu items. In the result, the total average accuracy was 0.900.

This work was supported by the Korea Research Foundation(KRF) grant funded by the Korea government(MEST) (No. 2011-0002899).

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul, Korea

    Yeong Hyeon Gu & Seong Joon Yoo

Authors
  1. Yeong Hyeon Gu
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  2. Seong Joon Yoo
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Editor information

Editors and Affiliations

  1. Department of Computer and Inforamtion Science, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau, China

    Zhiguo Gong

  2. School of Computer, Shanghai University, 200444, Shanghai, China

    Xiangfeng Luo

  3. College of Computer and Software, Taiyuan University of Technology, 030024, Taiyuan, China

    Junjie Chen

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  5. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Gu, Y.H., Yoo, S.J. (2011). Mining Popular Menu Items of a Restaurant from Web Reviews. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_31

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  • DOI: https://doi.org/10.1007/978-3-642-23982-3_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23981-6

  • Online ISBN: 978-3-642-23982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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