人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
原著論文
単語極性反転モデルによる評価文分類
池田 大介高村 大也奥村 学
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2010 年 25 巻 1 号 p. 50-57

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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.

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© 2010 JSAI (The Japanese Society for Artificial Intelligence)
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