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
DOI
https://dx.doi.org/10.1145/1348599.1348605
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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