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A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs

  • Michelle Annett
  • Grzegorz Kondrak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

Abstract

With the ever-growing popularity of online media such as blogs and social networking sites, the Internet is a valuable source of information for product and service reviews. Attempting to classify a subset of these documents using polarity metrics can be a daunting task. After a survey of previous research on sentiment polarity, we propose a novel approach based on Support Vector Machines. We compare our method to previously proposed lexical-based and machine learning (ML) approaches by applying it to a publicly available set of movie reviews. Our algorithm will be integrated within a blog visualization tool.

Keywords

Feature Vector Target Word Sentiment Analysis Negative Word Machine Learning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michelle Annett
    • 1
  • Grzegorz Kondrak
    • 1
  1. 1.Department of Computing ScienceUniversity of Alberta 

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