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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/120379
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/120379


    Title: Opinion Mining for Multiple Types of Emotion-Embedded Products/Services through Evolution Strategy
    Authors: Yang, Heng-Li;Lin, Qing-Feng
    楊亨利
    Yang, Heng-Li
    Contributors: 資管系
    Keywords: Chinese corpus;Evolutionary strategy;Multiple polarities;Opinion mining;Optimization;Sentiment analysis
    Date: 2018-06
    Issue Date: 2018-10-05 16:31:04 (UTC+8)
    Abstract: Since the advent of blogging, microblogging, and social networking sites, researchers and practitioners have been increasingly concerned with the problem of obtaining useful evaluations from web-based opinion articles in a process known as opinion mining or sentiment analysis. In this study, we focused on reviews based on highly emotion-embedded products/services, such as movies, music, and drama. Furthermore, we tried to solve the multiple polarities problem for the same review word for multiple types of product/service. First, we collected text written in Chinese from a Taiwanese movie forum. In our proposed approach, we applied an evolutionary strategy algorithm to optimize the weight tables corresponding to two different types of movies: horror and drama movies. The experimental results indicated that the proposed method performed better than conventional methods when considering only one generalized type. Further, we employed a new multi-class support vector machine approach for predicting opinions at the document level. We used seven measures to describe the characteristics of an overall document, including the central tendency, dispersion, and shape of the predicted sentence value distribution, where the fluctuations in these values corresponded to their positions in the document. We also demonstrated the effectiveness of this approach for identifying opinions at the document level.
    Relation: Expert Systems with Applications, Vol.99, pp.44-55
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.eswa.2018.01.022
    DOI: 10.1016/j.eswa.2018.01.022
    Appears in Collections:[資訊管理學系] 期刊論文

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