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Automatically Generating Aspect Taxonomy for E-Commerce Domains to Assist Sentiment Mining

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Security with Intelligent Computing and Big-data Services (SICBS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 733))

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Abstract

Numerous reviews are available online for many domains, and increasingly even for singular products. In this scenario, aspect associations to domains can be made extensive. Instead of generating aspects from the training set of reviews for a domain, the task of aspect generation is pushed onto an automated taxonomy generation system. Based on certain user input parameters, the taxonomy is expanded using an unsupervised web crawl of E-Commerce Website(s). The aspect taxonomy can be used to assist researchers in annotation of reviews to use for training classifiers for sentiment analysis, and for visualization of sentiment analysis results.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Guindy, Chennai, 600025, Tamil Nadu, India

    Nachiappan Chockalingam

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  1. Nachiappan Chockalingam
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Correspondence to Nachiappan Chockalingam .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  2. Department of Information Management, Central Police University, Taoyuan, Taiwan

    Shiuh-Jeng Wang

  3. Department of Automation and Applied Informatics, Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computer Technology, Jingzhou, China

    Ming Zhao

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Chockalingam, N. (2018). Automatically Generating Aspect Taxonomy for E-Commerce Domains to Assist Sentiment Mining. In: Peng, SL., Wang, SJ., Balas, V., Zhao, M. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2017. Advances in Intelligent Systems and Computing, vol 733. Springer, Cham. https://doi.org/10.1007/978-3-319-76451-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-76451-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76450-4

  • Online ISBN: 978-3-319-76451-1

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