Cross-domain sentiment analysis method based on GCN under lifelong learning framework

The invention provides a cross-domain sentiment analysis method based on GCN under a lifelong learning framework, relates to the field of cross-domain sentiment classification in natural language processing, and is realized by the following process: proposing a cross-domain sentiment classification algorithm based on a graph convolutional neural network, namely CDSGCN, on the basis of proposing the CDSGCN, proposing a cross-domain sentiment classification algorithm based on a graph convolutional neural network under a lifelong learning framework, namely LLCDSGCN in combination with a lifelonglearning thought, wherein the lifelong learning is different from transfer learning or multi-task learning and other related learning tasks due to the characteristics, the limitation of isolated learning is broken through, the influence caused by time-consuming and labor-consuming manual data annotation is relieved, and the characteristics are in accordance with the initial intention of cross-domain emotion classification