• DocumentCode
    669731
  • Title

    Comparison of different algorithms for sentiment classification

  • Author

    Ciric, Miroslav ; Stanimirovic, Aleksandar ; Petrovic, Nikola ; Stoimenov, Leonid

  • Author_Institution
    Fac. of Electron. Eng., Univ. of Nis, Niš, Serbia
  • Volume
    02
  • fYear
    2013
  • fDate
    16-19 Oct. 2013
  • Firstpage
    567
  • Lastpage
    570
  • Abstract
    Sentiment classification has various applications and information from social networks can be especially useful. In this paper we perform sentiment classification of Twitter messages, so called tweets. We compare several machine learning classification algorithms and try to improve results by using processing pipes that extract meaningful features and remove noise.
  • Keywords
    classification; learning (artificial intelligence); social networking (online); Twitter messages; machine learning classification algorithms; processing pipes; sentiment classification; social networks; tweets; Accuracy; Classification algorithms; Entropy; Machine learning algorithms; Nickel; Training; Twitter; Machine learning; Sentiment classification; Twitter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), 2013 11th International Conference on
  • Conference_Location
    Nis
  • Print_ISBN
    978-1-4799-0899-8
  • Type

    conf

  • DOI
    10.1109/TELSKS.2013.6704442
  • Filename
    6704442
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=669731