• DocumentCode
    3757966
  • Title

    Business Reviews Classification Using Sentiment Analysis

  • Author

    Andreea Salinca

  • Author_Institution
    Fac. of Math. &
  • fYear
    2015
  • Firstpage
    247
  • Lastpage
    250
  • Abstract
    The research area of sentiment analysis, opinion mining, sentiment mining and sentiment extraction has gained popularity in the last years. Online reviews are becoming very important criteria in measuring the quality of a business. This paper presents a sentiment analysis approach to business reviews classification using a large reviews dataset provided by Yelp: Yelp Challenge dataset. In this work, we propose several approaches for automatic sentiment classification, using two feature extraction methods and four machine learning models. It is illustrated a comparative study on the effectiveness of the ensemble methods for reviews sentiment classification.
  • Keywords
    "Feature extraction","Business","Sentiment analysis","Classification algorithms","Support vector machines","Training","Algorithm design and analysis"
  • Publisher
    ieee
  • Conference_Titel
    Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015 17th International Symposium on
  • Type

    conf

  • DOI
    10.1109/SYNASC.2015.46
  • Filename
    7426090