Transfer Learning by Linking Similar Feature Clusters for Sentiment Classification

Transfer learning aims to extract the knowledge from a label-rich source domain to enhance the predictive model of a target domain. Previous methods achieve knowledge transfer by detecting a shared low-dimensional feature representation from source domain to target domain. Along this line, many algorithms, e.g., dual transfer learning (DTL), triplex transfer learning (TRi-TL) etc., have been proposed and widely used for text classification. However, we argue that it is difficult for models to distinguish exactly the common concepts or identical concepts across different domains through the existing algorithms, even though source and target domains are related but different. So we propose to use the similar feature clusters as knowledge transfer, that is, we only guarantee the approximate similarity of common word clusters across different domains, rather than the exactly same. Based on the above assumption, the derived association matrices between word clusters and document classes should be slightly different to account for the word clusters variations. To take the above assumptions into account, we propose a novel Nonnegative Matrix Tri-Factorization based transfer learning by linking similar feature clusters (LSF-TL) for sentiment classification, in which an approximate constraint between similar word clusters matrices is added to allow differences while keeping the knowledge transferring function. Besides, LSF-TL also provides the same approximate constraint for the derived clusters association matrices. Then we employ an iterative updating algorithm with sound theoretical proof to find the local optimal solution. Last, we evaluate our method by conducting extensive experiments on Amazon product reviews. The results show that our approach achieves better classification accuracy than the state-of-the-art methods for both Cross-lingual sentiment classification(CLSC) and Cross-lingual cross-domain sentiment classification(CLCDSC) tasks.