Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Peng Chen, Zhongqian Sun, Lidong Bing, Wei Yang

Abstract
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications. The weighted-memory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence. We examine the merit of our model on four datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity. The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.
Anthology ID:
D17-1047
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
452–461
Language:
URL:
https://aclanthology.org/D17-1047
DOI:
10.18653/v1/D17-1047
Bibkey:
Cite (ACL):
Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent Attention Network on Memory for Aspect Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 452–461, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Recurrent Attention Network on Memory for Aspect Sentiment Analysis (Chen et al., EMNLP 2017)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/D17-1047.pdf
Attachment:
 D17-1047.Attachment.zip
Code
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Data
SemEval-2014 Task-4
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