English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 57621/91160 (63%)
造访人次 : 13560905      在线人数 : 65
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/101460

    题名: Using syntactic rules to combine opinion elements in Chinese opinion mining systems
    作者: Wu, Shih-Jung
    贡献者: 淡江大學資訊創新與科技學系
    关键词: Opinion mining;Sentiment analysis;Sentences pattern
    日期: 2015-03-31
    上传时间: 2015-04-30 19:16:52 (UTC+8)
    出版者: AICIT
    摘要: Most Chinese opinion mining systems use the specific pattern and nearby approach to combine relevant opinion elements (feature words and opinion words) to express the opinion tendencies of authors. In this paper, we propose a rule-based ad hoc method to study the combination problem of Chinese opinion elements. We extracted the opinion elements of articles based on lexicons and then combined them with the different sentence patterns and grammars to analyze the authors’ opinions. Because the articles on the online communities such as blogs, wikis, online forums, etc. do not have a defined format, there are often opinion comments that do not refer to the topic, resulting in information loss and significantly reduced recall. Therefore, the “default topic” method is proposed to correct this type of problem. Additionally, there might be errors when using the nearby approach to combine opinion elements. Thus, we propose the concept of “clause priority” to increase precision. After 20 months of long-term tracking and analysis, the experimental result indicates that the method proposed in this paper had good precision, recall, and F1 of opinion tendency analysis for review articles.
    關聯: Journal of Convergence Information Technology 10(2), p.137-144
    显示于类别:[資訊創新與科技學系] 期刊論文


    档案 描述 大小格式浏览次数
    JCIT4302PPL.pdf0.pdf897KbAdobe PDF493检视/开启



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈