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OntoLSA—An Integrated Text Mining System for Ontology Learning and Sentiment Analysis

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Sentiment Analysis and Ontology Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 639))

Abstract

Since the inception of the Web 2.0, World Wide Web is widely being used as a platform by customers and manufactures to share experiences and opinions regarding products, services, marketing campaigns, social events, etc. As a result, there is enormous growth in user-generated contents (e.g. customer reviews), providing an opportunity for data analysts to computationally evaluate users’ sentiments and emotions for developing real-life applications for business intelligence, product recommendation, enhanced customer services, and target marketing. Since users’ feedbaks (aka reviews) are very useful for products development and marketing, large business houses and corporates are taking interest in opinion mining and sentiment analysis systems. In this chapter, we propose the design of an Ontology Learning and Sentiment Analysis (OntoLSA) system for ontology learning and sentiment analysis using rule-based and machine learning approaches. The rule-based approach aims to identify candidate concepts, which are analyzed using a customized HITS algorithm to compile a list of feasible concepts. Feasible concepts and their relationships (both structural and non-structural) are used to generate a domain ontology, which is later on used for opinion mining and sentiment analysis . The proposed system is also integrated with a visualization module to facilitate users to navigate through review documents at different levels of granularity using a graphical user interface.

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Notes

  1. 1.

    http://conceptnet5.media.mit.edu.

  2. 2.

    http://opendovr.nl.

  3. 3.

    http://code.google.com/p/crawler4j.

  4. 4.

    http://nlp.stanford.edu/software/lex-parser.shtml.

  5. 5.

    http://code.google.com/apis/ajax/playground/#chart_wrapper.

  6. 6.

    http://www.json.org/.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Jamia Millia Islamia, New Delhi, 25, India

    Ahmad Kamal

  2. Department of Computer Science, Jamia Millia Islamia, New Delhi, 25, India

    Muhammad Abulaish &  Jahiruddin

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  1. Ahmad Kamal
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  2. Muhammad Abulaish
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  3. Jahiruddin
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Corresponding author

Correspondence to Muhammad Abulaish .

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Editors and Affiliations

  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

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Kamal, A., Abulaish, M., Jahiruddin (2016). OntoLSA—An Integrated Text Mining System for Ontology Learning and Sentiment Analysis. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-30319-2_16

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  • Print ISBN: 978-3-319-30317-8

  • Online ISBN: 978-3-319-30319-2

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