Paper: Learning to Shift the Polarity of Words for Sentiment Classification

ACL ID I08-1039
Title Learning to Shift the Polarity of Words for Sentiment Classification
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008
Authors

We propose a machine learning based method of sentiment classification of sen- tences using word-level polarity. The polari- ties of words in a sentence are not always the same as that of the sentence, because there can be polarity-shifters such as negation ex- pressions. 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 be combined with features used in previous work such as bag-of-words and n-grams. We empiri- cally show that our method almost always improves the performance of sentiment clas- sification of sentences especially when we have only small amount of trainin...