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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51455

Title: An Opinion Feature Extraction Approach Based on a Multidimensional Sentence Analysis Model
Authors: Jiunn-Liang Guo
Jhih-En Peng
Hei-Chia Wang
Contributors: 國立臺灣海洋大學:商船學系
Keywords: multidimensional sentence analysis
opinion mining
text summarization
Date: 2013-10
Issue Date: 2018-11-29T03:46:39Z
Publisher: Cybernetics and Systems: An International Journal
Abstract: Abstract: With Web 2.0 applications being widely used, social networking services, including web blogs, forums, and other online communities, have become informative tools that help individuals to easily gauge the pulse of the electronic consuming market. As a substitute for traditional public media, the related site provides unique mechanisms to instantly reveal the degree of public product acceptance by either statistically aggregating the rating results or archiving opinions shared by experienced customers. However, the growth of user-generated information and its scattered unstructured contents is overwhelming to users, thereby triggering the demand for a more efficient system that can offer concise information. Most existing efforts dedicated to these issues may neglect vital aspects of the sentence-level context. This article aims to explore the critical features hidden in the sentential structure of opinion articles and expects that the detected patterns may contribute to the enhancement of related applications. Accordingly, a multidimensional sentence modeling algorithm (MSMA) is designed to evaluate various sentential characteristics and adopt a genetic algorithm to optimize the weighting scheme while determining feature importance. The study also makes use of the public knowledge resource Wikipedia as a global reference to fine-tune the feature set's effectiveness and enhance the overall performance of this framework. The results of experiments on an electronic product data set demonstrate that the proposed method is promising and provides significant improvement over previous studies.
Relation: 44(5) pp.379-401
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51455
Appears in Collections:[商船學系] 期刊論文

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