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A hierarchical approach to mood classification in blogs

Published online by Cambridge University Press:  11 March 2011

FAZEL KESHTKAR
Affiliation:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada e-mail: akeshta@site.uOttawa.ca, diana@site.uOttawa.ca
DIANA INKPEN
Affiliation:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada e-mail: akeshta@site.uOttawa.ca, diana@site.uOttawa.ca

Abstract

In this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

Type
Articles
Copyright
Copyright © Cambridge University Press 2011

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