Feature Selection Method for Semi-Supervised Sentiment Classification
Wang Zhiha · Zhongwen xinxi xuebao · 2013
Feature selection aims to reduce the high-dimensional feature space so as to simplify the problem and improve the learning method.Existing studies have shown that feature selection is effective in reducing feature space in sentiment classification.In this paper,we focus on feature selection method.Different from all previous studies,we attempt to conduct the research on feature selection on semi-supervised sentiment classification.We propose a novel feature selection method based on bipartite graph which focuses on semi-supervised sentiment classification.First, we formulate the relations between documents and words with the help of bipartite graph model.Then,with a small amount of labeled data and the bipartite graph,a label propagation algorithm is applied to calculate the feature probabilities belonging to sentimental categories.Third,the features are then selected according the sentimental probabilities.The experimental results across multiple domains demonstrate that our feature selection method achieves much better performances than random feature selection method.Our approach is capable of significantly reducing the dimension of the feature vector without any loss in the classification performance.