Deep2s: Improving Aspect Extraction in Opinion Mining With Deep Semantic Representation

Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surface sentence structures. However, deep and rich semantic information hidden in sent...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.104026-104038
Main Authors: Li, Xuelian, Wang, Bi, Li, Lixin, Gao, Zhiqiang, Liu, Qian, Xu, Hancheng, Fang, Lanting
Format: Article
Language:English
Subjects:
abstract meaning representation
Annotations
aspect extraction
Batteries
Cameras
Data mining
deep semantic representation
Dependence
Feature extraction
Logic programming
Opinion mining
Performance enhancement
Representations
Semantics
Sentiment analysis
Surface structure
Syntactics
Task analysis
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Summary:Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surface sentence structures. However, deep and rich semantic information hidden in sentences which can help improve aspect extraction, is difficult for them to capture. In order to address the problem, this paper first proposes to employ Logic Programming to explore the feasibility of deep semantic representation, then proposes Deep2S, a hybrid rule-based method to improve the performance of aspect extraction. Deep2S integrates deep semantic representation such as Abstract Meaning Representation (AMR) with syntactic structure. It can take advantage of the syntactical rules to obtain dependency relations in the surface structure as well as the semantic rules to capture deep semantic information. Our experiments are conducted on eight popular review datasets using two evaluation metrics. Experimental results demonstrate the usefulness of deep semantic representation and the ability of Deep2S to improve the performance of aspect extraction in opinion mining.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2999673