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Title: Sentiment classification with support vector machines and multiple kernel functions
Authors: Tanasanee Phienthrakul
Boonserm Kijsirikul
Hiroya Takamura
Manabu Okumura
Mahidol University
Chulalongkorn University
Tokyo Institute of Technology
Keywords: Computer Science;Mathematics
Issue Date: 1-Dec-2009
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.5864 LNCS, No.PART 2 (2009), 583-592
Abstract: Support vector machine (SVM) is a learning technique that performs well on sentiment classification. The performance of SVM depends on the used kernel function. Hence, if the suitable kernel is chosen, the efficiency of classification should be improved. There are many approaches to define a new kernel function. Non-negative linear combination of multiple kernels is an alternative, and the performance of sentiment classification can be enhanced when the suitable kernels are combined. In this paper, we analyze and compare various non-negative linear combination kernels. These kernels are applied on product reviews to determine whether a review is positive or negative. The results show that the performance of the combination kernels that outperforms the single kernels. © 2009 Springer-Verlag Berlin Heidelberg.
ISSN: 16113349
Appears in Collections:Scopus 2006-2010

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