An Improved Spectral Feature Alignment for Domain Adaptation in Sentiment Classification

Abstract

The problem of sentiment classification is extremely sensitive to the variation of domain, thus the sentiment classification model trained in one domain is often not applicable to the data from another domain. This paper proposes an improved spectral feature alignment domain adaptation algorithm (ISFA). ISFA extracts domain-independent words by term frequency and mutual information. Based on word co-occurrence relation, the bipartite graph between domain-independent words and domain-specific words is initially constructed. Then revision of sentiment dictionary is introduced to obtain a more accurate bipartite graph. In this graph, feature representation is obtained by utilizing spectral clustering dimension reduction. Besides, a new feature representation is obtained by combination updating for GBDT training. In this paper, 20 groups of experiments show that the accuracy of ISFA is 3.4% higher than that of SFA on average, which proves that ISFA is capable of finding out the feature space that makes the distribution of the two domains much closer.