A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A vector space model for automatic indexing
Communications of the ACM
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Data mining and audience intelligence for advertising
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Multimodal subjectivity analysis of multiparty conversation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Document sentiment classification by exploring description model of topical terms
Computer Speech and Language
| Hi-index | 0.00 |
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.