Sentiment classification with interpolated information diffusion kernels
conference paper
Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of experiments addressing the well-known movie review data set of Pang and Lee, a de facto benchmark, comparing information diffusion kernels with a standard RBF kernel machine. Our results show that interpolation of unigram and bigram information is beneficiary for sentiment classification. Copyright 2007 ACM.
Topics
TNO Identifier
364461
Source title
1st International Workshop on Data Mining and Audience Intelligence for Advertising, ADKDD 2007, Held in Conjunction with SIGKDD'07. 12 August 2007, San Jose, CA.
Pages
34-39
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