Journal
IEEE ACCESS
Volume 8, Issue -, Pages 29238-29248Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2972697
Keywords
Aspect-level sentiment classification; attention mechanism; interactive representation; natural language processing
Categories
Funding
- National Natural Science Foundation of China [61632011, 61573231, 61672331, 61906112, 61603229]
- Key Research and Development Program of Shanxi Province through the International Cooperation [201803D421024, 201903D421041]
- Natural Science Foundation of Shanxi Province [201901D211174]
- Scienti~c and Technological Innovation Programs of Higher Education Institutions in Shanxi [2019L0008]
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Aspect-level sentiment classification aims to determine sentiment polarities of various aspects in reviews, where each review typically contains multiple aspects, that may correspond to different polarities. Aspect-level sentiment classification, unlike document-level sentiment classification, requires different context representations for different aspects. Existing methods normally use Long Short-Term Memory (LSTM) network to model aspects and contexts separately, and they combine attention mechanisms to extract features of a specific aspect in its context. Attention mechanisms are not used for sequence modeling, so aspects are not considered when generating context sequence representations. This study proposes a novel aspect-context interactive representation structure that only relies on an attention mechanism for generating sequence-to-sequence representations in both context and aspect. It is capable of extracting features related to the specific aspect during the process of its context sequence modeling, and generating a high quality aspect representation simultaneously. We have conducted comprehensive experiments to compare with thirteen existing methods. Our experimental results show that the proposed model is able to achieve significantly better performance on Restaurant dataset, as well as very competitive results on Laptop and Twitter datasets.
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