• DocumentCode
    1645242
  • Title

    Aspect based Sentiment Analysis using support vector machine classifier

  • Author

    Varghese, Raisa ; Jayasree, M.

  • Author_Institution
    Dept. of Comput. Sci., Gov. Eng. Coll., Trichur, India
  • fYear
    2013
  • Firstpage
    1581
  • Lastpage
    1586
  • Abstract
    Sentiment Analysis involves the process of identifying the polarity of opinionated texts. Lots of social networking sites are being used for expressing thoughts and opinions by users to rate products. These user opinionated text is highly unstructured in nature and thus involves the application of various natural language processing techniques. In aspect based sentiment analysis, the various features of a product is identified through the training process. For e.g. the aspects of a camera are picture quality, size, resolution etc. The quantitative analysis of each aspect is done using support vector machine classifier. In most of the previous works, a product review is analysed as a whole rather than considering each aspect of it. Aspect based opinion mining is tedious since the identification of individual features is in itself a challenging task.
  • Keywords
    data mining; natural language processing; pattern classification; social networking (online); support vector machines; text analysis; aspect based opinion mining; aspect based sentiment analysis; feature identification; natural language processing techniques; social networking sites; support vector machine classifier; training process; user opinionated text polarity; Accuracy; Cameras; Feature extraction; Informatics; Natural language processing; Support vector machines; Training; Aspect Selection; Machine Learning; Natural Language Processing; Sentiment Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on
  • Conference_Location
    Mysore
  • Print_ISBN
    978-1-4799-2432-5
  • Type

    conf

  • DOI
    10.1109/ICACCI.2013.6637416
  • Filename
    6637416