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2019 | OriginalPaper | Chapter

Co-attention Networks for Aspect-Level Sentiment Analysis

Authors : Haihui Li, Yun Xue, Hongya Zhao, Xiaohui Hu, Sancheng Peng

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Aspect-level sentiment analysis has identified its significance in sentiment polarity classification of consumer review. For the purpose of specific target sentiment analysis, we put forward a co-attentive deep learning method in the manner of human processing. To start with, the GRUs are taken to extract the hidden states of the different word embeddings. Further, via the interactive learning of the co-attention network, the representations of the target and the context can be obtained. In addition, the attention weights are determined based on the self-attention mechanism to update the final representations. The experimental results evaluated on the SemEval 2014 and Twitter establish a strong evidence of the high accuracy.

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previous chapter Extending the Transformer with Context and Multi-dimensional Mechanism for Dialogue Response Generation

next chapter Hierarchical-Gate Multimodal Network for Human Communication Comprehension

The detail introduction of this task can be seen at: http://alt.qcri.org/semeval2014/task4/.

Pre-trained word vectors of Glove can be obtained from http://nlp.stanford.edu/projects/glove/.

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Title: Co-attention Networks for Aspect-Level Sentiment Analysis
Authors: Haihui Li
Yun Xue
Hongya Zhao
Xiaohui Hu
Sancheng Peng
Publisher: Springer International Publishing
Book: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-32235-9

Electronic ISBN: 978-3-030-32236-6

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-32236-6_17

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