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 be combined with features used in previous work such as bag-of-words and n-grams. We empirically show that our method almost always improves the performance of sentiment classification of sentences especially when we have only small amount of training data.
REF proposed a machine learning approach to handle sentiment polarity reversal.
Learning to Shift the Polarity of Words for Sentiment Classification
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science