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    A Bayesian Sampling Method for Product Feature Extraction From Large Scale Textual Data 

    Source: Journal of Mechanical Design:;2016:;volume( 138 ):;issue: 006:;page 61403
    Author(s): Lim, Sunghoon; Tucker, Conrad S.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The authors of this work propose an algorithm that determines optimal search keyword combinations for querying online product data sources in order to minimize identification errors during the product feature extraction ...
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    Automated Discovery of Product Feature Inferences Within Large-Scale Implicit Social Media Data 

    Source: Journal of Computing and Information Science in Engineering:;2018:;volume( 018 ):;issue: 002:;page 21017
    Author(s): Tuarob, Suppawong; Lim, Sunghoon; Tucker, Conrad S.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Recently, social media has emerged as an alternative, viable source to extract large-scale, heterogeneous product features in a time and cost-efficient manner. One of the challenges of utilizing social media data to inform ...
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    DSpace software copyright © 2002-2015  DuraSpace
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