文章基本信息

标题：Learning to Shift the Polarity of Words for Sentiment Classification
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作者：Daisuke Ikeda ; Hiroya Takamura ; Manabu Okumura 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2010
卷号：25
期号：1
页码：50-57
DOI：10.1527/tjsai.25.50
出版社：The Japanese Society for Artificial Intelligence
摘要：We propose a machine learning based method of sentiment classification of sentences using word-level polarity. The polarities of words in a sentence are not always the same as that of the sentence, because there can be polarity-shifters such as negation expressions. The proposed method models the polarity-shifters. Our model can be trained in two different ways: word-wise and sentence-wise learning. In sentence-wise learning, the model can be trained so that the prediction of sentence polarities should be accurate. The model can also combined with features used in previous work such as bag-of-words and n-grams. We empirically show that our method improves the performance of sentiment classification of sentences especially when we have only small amount of training data.
关键词：sentiment analysis ; sentence classification ; structure output learning