Feature Selection for Sentiment Classification Using Matrix Factorization

Feature selection is a critical task in both sentiment classification and topical text classification. However, most existing feature selection algorithms ignore a significant contextual difference between them that sentiment classification is commonly depended more on the words conveying sentiments. Based on this observation, a new feature selection method based on matrix factorization is proposed to identify the words with strong inter-sentiment distinguish-ability and intra-sentiment similarity. Furthermore, experiments show that our models require less features while still maintaining reasonable classification accuracy.