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- W2134264955 abstract "Polarity shifting marked by various linguistic structures has been a challenge to automatic sentiment classification. In this paper, we propose a machine learning approach to incorporate polarity shifting information into a document-level sentiment classification system. First, a feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences. Then, by using the obtained binary classifier, each document in the original polarity classification training data is split into two partitions, polarity-shifted and polarity-unshifted, which are used to train two base classifiers respectively for further classifier combination. The experimental results across four different domains demonstrate the effectiveness of our approach." @default.
- W2134264955 created "2016-06-24" @default.
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- W2134264955 date "2010-08-23" @default.
- W2134264955 modified "2023-09-27" @default.
- W2134264955 title "Sentiment Classification and Polarity Shifting" @default.
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