Tree-Structured Neural Networks With Topic Attention for Social Emotion Classification

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 som...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.95505-95515
Main Authors: Wang, Chang, Wang, Bang, Xu, Minghua
Format: Article
Language:English
Subjects:
Classification
Computer architecture
Correlation
Correlation coefficients
Emotions
Histograms
Long short-term memory
Neural networks
Representations
Semantics
Sentences
social emotion classification
Syntactics
Task analysis
topic attention mechanism
topic model
tree-structured neural network
Weighting
Words (language)
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2929204