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.
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Annett, M., Kondrak, G. (2008). A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_3
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DOI: https://doi.org/10.1007/978-3-540-68825-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
Online ISBN: 978-3-540-68825-9
eBook Packages: Computer ScienceComputer Science (R0)