Abstract
We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but a continuous measure called sentiment polarity score (sp-score). An sp-score gives a very concise summary of a review and provides more information than binary classification. The difficulty of this task lies in the quantification of polarity. In this paper we use support vector regression (SVR) to tackle the problem. Experiments on book reviews with five-point scales show that SVR outperforms a multi-class classification method using support vector machines and the results are close to human performance.
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Okanohara, D., Tsujii, J. (2005). Assigning Polarity Scores to Reviews Using Machine Learning Techniques. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_28
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DOI: https://doi.org/10.1007/11562214_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
Online ISBN: 978-3-540-31724-1
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