• DocumentCode
    3109486
  • Title

    Automatic parsing of the metaphor polarity for opinion mining

  • Author

    Xiaoying Xu ; Ya Li ; Jianhua Tao ; Xuefei Liu

  • Author_Institution
    Chinese Language & Literature Dept., Beijing Normal Univ., Beijing, China
  • fYear
    2012
  • fDate
    9-12 Dec. 2012
  • Firstpage
    13
  • Lastpage
    17
  • Abstract
    In recent years, both metaphor interpretation and opinion mining have drawn much attention in the natural language processing (NLP) field. This paper aims to make a connection between these two fields. In this paper, we propose to extend the glossary orientation annotation to the vehicle (the source concept part of the metaphor) by using an automatic annotation method, and based on the vehicle´s orientation corpus, we parse the metaphor´s polarity after extracting the metaphor(especially the simile)from the large-scale corpus. Two experiments are conducted to investigate the reliability of our proposal. The result of the first experiment shows the proposed method obtains better results than the system we proposed in 2009 in both precision and recall, while the result of second experiment shows that more than 65% metaphors have a very stable sentiment orientation. Generally, the results demonstrate the effectiveness of our approach and verifying our approach´s high reliability.
  • Keywords
    data mining; grammars; natural language processing; NLP; automatic annotation method; glossary orientation annotation; metaphor interpretation; metaphor polarity parsing; natural language processing; opinion mining; sentiment orientation; vehicle orientation corpus; Educational institutions; Equations; Natural language processing; Semantics; Stability analysis; Vehicles; metaphor polarity parsing; opinion mining; vehicle orientation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Speech Database and Assessments (Oriental COCOSDA), 2012 International Conference on
  • Conference_Location
    Macau
  • Print_ISBN
    978-1-4673-2811-1
  • Electronic_ISBN
    978-1-4673-2812-8
  • Type

    conf

  • DOI
    10.1109/ICSDA.2012.6422465
  • Filename
    6422465