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Open AccessArticle

A Multi-Attention Network for Aspect-Level Sentiment Analysis

by Qiuyue Zhang and Ran Lu *
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(7), 157; https://doi.org/10.3390/fi11070157
Received: 17 June 2019 / Revised: 11 July 2019 / Accepted: 11 July 2019 / Published: 16 July 2019
(This article belongs to the Section Network Virtualization and Edge/Fog Computing)
Aspect-level sentiment analysis (ASA) aims at determining the sentiment polarity of specific aspect term with a given sentence. Recent advances in attention mechanisms suggest that attention models are useful in ASA tasks and can help identify focus words. Or combining attention mechanisms with neural networks are also common methods. However, according to the latest research, they often fail to extract text representations efficiently and to achieve interaction between aspect terms and contexts. In order to solve the complete task of ASA, this paper proposes a Multi-Attention Network (MAN) model which adopts several attention networks. This model not only preprocesses data by Bidirectional Encoder Representations from Transformers (BERT), but a number of measures have been taken. First, the MAN model utilizes the partial Transformer after transformation to obtain hidden sequence information. Second, because words in different location have different effects on aspect terms, we introduce location encoding to analyze the impact on distance from ASA tasks, then we obtain the influence of different words with aspect terms through the bidirectional attention network. From the experimental results of three datasets, we could find that the proposed model could achieve consistently superior results. View Full-Text
Keywords: aspect-level; sentiment analysis; multi-attention aspect-level; sentiment analysis; multi-attention
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Zhang, Q.; Lu, R. A Multi-Attention Network for Aspect-Level Sentiment Analysis. Future Internet 2019, 11, 157.

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