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    A sentiment-based meta search engine

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    Author
    Na, Jin-Cheon
    Khoo, Christopher S.G.
    Chan, Syin
    Editors
    Khoo, C.
    Singh, D.
    Chaudhry, A.S.
    Issue Date
    2006
    Submitted date
    2007-05-22
    Keywords
    Classification
    Web Mining
    Information Retrieval
    Natural Language Processing
    Local subject classification
    Sentiment classification
    Meta search engine
    
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    Citation
    A sentiment-based meta search engine 2006, :83-89
    Publisher
    School of Communication & Information, Nanyang Technological University
    URI
    http://hdl.handle.net/10150/106241
    Abstract
    This study is in the area of sentiment classification: classifying online review documents according to the overall sentiment expressed in them. This paper presents a prototype sentiment-based meta search engine that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended or non-recommended information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents. It does this by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM). This paper also discusses various issues we have encountered during the prototype development, and presents our approaches for resolving them. A user evaluation of the prototype was carried out with positive responses from users.
    Type
    Conference Paper
    Language
    en
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