Pacific Asia Conference on Language, Information, and Computation · 2007 · 47 citations · 11 references
EngineeringMachine LearningSingle ClassifierMultimodal Sentiment AnalysisCorpus LinguisticsSentiment AnalysisJournalismText MiningNatural Language ProcessingCustomer ReviewInformation RetrievalData MiningDocument ClassificationContent AnalysisMovie Review DocumentsAutomatic ClassificationMultiple ClassifierKnowledge DiscoveryArtsOpinion Aggregation
In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.
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Leo Breiman · Machine Learning · 2001 · 119.3K citations · Full text
Yuhai Wu, Vladimir Vapnik · Technometrics · 1999 · 26.9K citations
Leo Breiman · Machine Learning · 1996 · 16.6K citations · Full text
Experiments with a new boosting algorithm
Yoav Freund, Robert E. Schapire · 1996 · 7.6K citations
Bo Pang, Lillian Lee, Shivakumar Vaithyanathan · 2002 · 7K citations · Full text
Engineering, Maximum Entropy Classification, Multimodal Sentiment Analysis +18