Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees
作者：
Highlights：
• Improves sentiment analysis with discourse trees from rhetoric structure theory.
• Extracts salient passages based on the position and relation in the discourse tree.
• Develops a tensor-based tree-structured neural network.
• Tensor structure distinguishes hierarchy and relation types.
• Overfitting is reduced by a tree-based algorithms for data augmentation.
摘要
•Improves sentiment analysis with discourse trees from rhetoric structure theory.•Extracts salient passages based on the position and relation in the discourse tree.•Develops a tensor-based tree-structured neural network.•Tensor structure distinguishes hierarchy and relation types.•Overfitting is reduced by a tree-based algorithms for data augmentation.
论文关键词：Sentiment analysis,Rhetorical structure theory,Discourse tree,Tree-structured network,Long short-term memory,Tensor-based network
论文评审过程：Received 9 July 2018, Revised 1 October 2018, Accepted 2 October 2018, Available online 4 October 2018, Version of Record 9 October 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.10.002
