With enough labeled data lacking in the target domain

it works well for transfer learning to use the labeled data of the related source domain and help improve the learning performance of the target domain. However

the data of these two domains usually do not satisfy the independently identically distribution

which easily leads to the problem of "negative transfer". Tr-SLDA (Transfer SLDA)

a novel transfer topic model based on supervised topic model (Supervised LDA

SLDA) is proposed

which shares topic knowledge by integrating transfer learning. A new Tr-SLDA-Gibbs sampling method is proposed

under the constraints of category labels

different sampling strategies are adopted for words in the documents of different domains without specifying the number of topics. The source domain and target domain share the potential topic space

Tr-SLDA can effectively solve the problem of "negative transfer" by discovering the semantic correlation between the potential shared topics and categories of different domains. The Tr-SLDA-TC (Tr-SLDA-Text Categorization) text classification method is proposed based on the Tr-SLDA model. The comprehensive experiments show that the proposed method can effectively improve the performance of the classification by utilizing the knowledge from the source domain.