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• W73617575 abstract "AbstractSentiment analysis is an area of research that has gained considerable attention in recent years due to the increasing availability of opinionated information online. The majority of the work in sentiment analysis considers the polarity of word terms rather than the polarity of specific senses of the word but different senses of a word can have different opinion-related properties. In order to address this issue we consider novel semantic features of words in the context of a sentence. We take a sentence as a sequence of words augmented with features based on word sense disambiguation and sentiment lexicons with sense specific opinion-related properties. We then use a factored version of the sequence kernel in a support vector machine, and apply it to sentiment classification of sentences. We evaluate this sentiment analysis methodology on three publicly available corpuses. We also evaluate the effectiveness of several publicly available sense specific polarity lexicons and combinations. Experiments show that our factored approach offers improvements over the surface words baseline and other state-of-the-art kernels.KeywordsInformation RetrievalSocial MediaSentiment AnalysisOpinion MiningPolarity ClassificationKernel MethodsWord Sense Disambiguation" @default.
• W73617575 created "2016-06-24" @default.
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• W73617575 date "2013-01-01" @default.
• W73617575 modified "2023-09-27" @default.
• W73617575 title "Factored Semantic Sequence Kernel for Sentiment Polarity Classification" @default.
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• W73617575 doi "https://doi.org/10.1007/978-3-642-39593-2_25" @default.
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