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- W2964202476 abstract "Social emotion classification studies the emotion distribution evoked by an article among numerous readers. Although recently neural network-based methods can improve the classification performance compared with the previous word-emotion and topic-emotion approaches, they have not fully utilized some important sentence language features and document topic features. In this paper, we propose a new neural network architecture exploiting both the syntactic information of a sentence and topic distribution of a document. The proposed architecture first constructs a tree-structured long short-term memory (Tree-LSTM) network based on the sentence syntactic dependency tree to obtain a sentence vector representation. For a multi-sentence document, we then use a Chain-LSTM network to obtain the document representation from its sentences’ hidden states. Furthermore, we design a topic-based attention mechanism with two attention levels. The word-level attention is used for weighting words of a single-sentence document and the sentence-level attention for weighting sentences of a multi-sentence document. The experiments on three public datasets show that the proposed scheme outperforms the state-of-the-art ones in terms of higher average Pearson correlation coefficient and MicroF1 performance." @default.
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- W2964202476 date "2019-01-01" @default.
- W2964202476 modified "2023-09-25" @default.
- W2964202476 title "Tree-Structured Neural Networks With Topic Attention for Social Emotion Classification" @default.
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- W2964202476 doi "https://doi.org/10.1109/access.2019.2929204" @default.
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