基于内联关系的方面级情感分析方法

doi:10.12066/j.issn.1007-2861.2265

Abstract

Abstract:

Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity of particular entities in given sentences. Studies show that the most effective methods use features obtained by modelling entities and their contexts with attention to sentiment prediction. However, these methods calculate the attention weights of contexts by using mean vectors for target entities. In addition, these methods cannot highlight the importance of individual words in the text of whole sentences. Therefore, this study proposes an aspect-based sentiment analysis method that can model the inner relations with memory networks, where networks can learn more effective contextual representations. First, gated recurrent units (GRUs) are used to embed the distributed representations of aspect words and words in sentences. Then, a long short-term memory network (LSTM) takes the distributed representation as input to increase the weights of entities according to the attention-based contextual relationship. Finally, through a query mechanism, aspect-based sentiment polarity is obtained. The proposed model is tested on the open-source datasets Semeval-2014 and Semeval-2016, with results showing that the proposed method is effective and the accuracy is higher than that of the baselines.

Key words: aspect, sentiment analysis, inner relation, gated recurrent unit (GRU), long short-term memory (LSTM)

CLC Number:

TP183

ZHANG Ke, ZHANG Wenjun, ZHU Yunwen, XING Yixue. Inner-relation modelling with memory networks in aspect-based sentiment analysis[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(1): 157-169.

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URL: https://www.journal.shu.edu.cn/EN/10.12066/j.issn.1007-2861.2265

https://www.journal.shu.edu.cn/EN/Y2022/V28/I1/157

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Inner-relation modelling with memory networks in aspect-based sentiment analysis

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