Journal
NATURAL LANGUAGE ENGINEERING
Volume 18, Issue -, Pages 61-81Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S1351324911000118
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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.
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