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Constraint-based Ontology Induction from Online Customer Reviews

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  • Thomas Lee

    (University of Pennsylvania)

Abstract

We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process.

Suggested Citation

  • Thomas Lee, 2007. "Constraint-based Ontology Induction from Online Customer Reviews," Group Decision and Negotiation, Springer, vol. 16(3), pages 255-281, May.
    • Handle: RePEc:spr:grdene:v:16:y:2007:i:3:d:10.1007_s10726-006-9065-3
    DOI: 10.1007/s10726-006-9065-3
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    Cited by:

    1. Hui Zhang & Huguang Rao & Junzheng Feng, 2018. "Product innovation based on online review data mining: a case study of Huawei phones," Electronic Commerce Research, Springer, vol. 18(1), pages 3-22, March.

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