単語極性反転モデルによる評価文分類 [in Japanese] Learning to Shift the Polarity of Words for Sentiment Classification [in Japanese]
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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.
- Transactions of the Japanese Society for Artificial Intelligence
Transactions of the Japanese Society for Artificial Intelligence 25(1), 50-57, 2010
The Japanese Society for Artificial Intelligence