Product attribute extraction is a key point in sentiment analysis. In this paper, a product attribute extraction method based on feature selection and pointwise mutual information pruning strategies is proposed. Firstly, the extraction task is transferred to a feature selection task in a classifier. The classification model with l1-norm regularization, such as Lasso, can encourage a sparse model with fewer important selected features. Secondly, some extracted features are selected through a frequency threshold. The features as the product attributes are finally generated with point mutual information pruning. The experiments on the product reviews in Chinese demonstrate the effectiveness of the proposed method.
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2015, Vol. 28 
