Opinion retrieval text aims to retrieve the opinion documents related to a query from a specific doc-ument set,which involves the combination of query and document relevance as well as the orientation of the document itself.The existing models focus on mining opinion features based on traditional machine learning when extracting document opinion features.These methods ignore the information representation of adjacent word nodes in the document,which leads to limited generalization ability at the semantic level of the text and cannot obtain more accurate opinion features,thus affecting the opinion retrieval performance.To solve the a-bove problems,this paper proposes an opinion retrieval method based on graph convolutional network,which aims to map the words in the document into a high-dimensional semantic space,adaptively learn the connection relationship between the document word nodes,and obtain more accurate semantic information representation of the document.Experiments show that,compared with the current best model,the improved model improves the main index by 1.2%and 0.8%respectively on two Twitter public data,which well verifies the effectiveness of the proposed method.
维普
万方数据