Sentiment Analysis with Abstract Meaning Representation and Dependency Grammar

被引：0
作者：
Li X. [1 ]
Wang B. [2 ]
Li L. [3 ]
Han D. [4 ]
机构：
[1] School of Foreign Studies, Nanjing University of Posts and Telecommunications, Nanjing
[2] School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou
[3] School of Computer Science and Engineering, Southeast University, Nanjing
[4] School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta
来源：
Data Analysis and Knowledge Discovery | 2024年 / 8卷 / 01期
关键词：
Abstract Meaning Representation; Aspect-Based Sentiment Analysis; Dependency Grammar; Rule;
D O I：
10.11925/infotech.2096-3467.2022.1259
中图分类号：
学科分类号：
摘要：
[Objective] This paper aims to combine the deep semantic representation and surface syntactic structure of natural language sentences. [Methods] We proposed an integration strategy based on semantic and syntactic rule concatenation and utilized it for the aspect-based sentiment analysis. This strategy used the answer set programming language (ASP) to represent abstract meaning representation (AMR), dependency grammar (DEP), and part of speech (POS) as ASP facts. It also integrated the DEP and POS through rule body extension based on AMR rules. Therefore, a sentence’s two or more language features were concatenated into the rule body. Based on this strategy, we developed the AMR-DEP-POS-C and AMR-DEP-C models. [Results] We examined the new methods on eight publicly available review datasets. The AMR-DEP-POS-C achieved a complementary relationship between semantics and syntax and performed better than the baseline methods based on semantic, syntactic, and deep learning. [Limitations] Our new models rely on the accuracy of the existing AMR and DEP parsers. [Conclusions] AMR-DEP-POS-C can effectively integrate different language features and bring new research perspectives and tools for aspect-based sentiment analysis. © 2024 Chinese Academy of Sciences. All rights reserved.
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页码：55 / 68
页数：13
相关论文
共 47 条
  • [1] Pang B, Lee L., Opinion Mining and Sentiment Analysis, Foundations and Trends in Information Retrieval, 2, 1-2, pp. 1-135, (2008)
  • [2] Tan Ying, Zhang Jin, Xia Lixin, A Survey of Sentiment Analysis on Social Media, Data Analysis and Knowledge Discovery, 4, 1, pp. 1-11, (2020)
  • [3] Zhong Jiawa, Liu Wei, Wang Sili, Et al., Review of Methods and Applications of Text Sentiment Analysis, Data Analysis and Knowledge Discovery, 5, 6, pp. 1-13, (2021)
  • [4] Shang Rongxuan, Zhang Bin, Mi Jianing, End-to-End Aspect-Level Sentiment Analysis for E-Government Applications Based on BRNN, Data Analysis and Knowledge Discovery, 6, 2, pp. 364-375, (2022)
  • [5] Rastegar-Mojarad M, Elayavilli R K, Wang Y S, Et al., Semantic Information Retrieval: Exploring Dependency and Word Embedding Features in Biomedical Information Retrieval, Proceedings of the 2017 BioCreative VI Challenge Evaluation Workshop, pp. 74-77, (2017)
  • [6] Wang Y S, Liu S J, Rastegar-Mojarad M, Et al., Dependency and AMR Embeddings for Drug-Drug Interaction Extraction from Biomedical Literature, Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 36-43, (2017)
  • [7] Marcheggiani D, Bastings J, Titov I., Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks [C], Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 486-492, (2018)
  • [8] Gebser M, Kaminski R, Kaufmann B, Et al., Answer Set Solving in Practice, (2012)
  • [9] Son T C, Baral C, Tran N, Et al., Domain-Dependent Knowledge in Answer Set Planning, ACM Transactions on Computational Logic, 7, 4, pp. 613-657, (2002)
  • [10] Baral C., Knowledge Representation, Reasoning, and Declarative Problem Solving, (2003)
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