A novel weight-oriented graph convolutional network for aspect-based sentiment analysis-Reference-Cited by-同舟云学术

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A novel weight-oriented graph convolutional network for aspect-based sentiment analysis

Published:2022-07-20 Issue:1 Volume:79 Page:947-972
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Yu Bengong^ORCID,Zhang Shuwen

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-022-04689-9.pdf

Reference63 articles.

1. Fu Y, Shi W (2018) Research on sentiment analysis of micro-blog based on enhanced supervised learning. J Intell 37(12):130–134167

2. Zhao J, Liu K, Xu L (2016) Sentiment analysis: Mining opinions, sentiments, and emotions. Comput Linguist 42(3):595–598. https://doi.org/10.1162/COLI_r_00259

3. Wang Y, Zhu J, Wang Z, Gong J (2021) Review of applications of natural language processing in sentiment analysis. Journal of Computer Applications, 1–12. https://doi.org/10.11772/j.issn.1001-9081.2021071262

4. Hameed Z, Garcia-Zapirain B (2020) Sentiment classification using a single-layered bilstm model. IEEE Access 8:73992–74001. https://doi.org/10.1109/ACCESS.2020.2988550

5. Zhang Y, Zheng J, Jiang Y, Huang G, Chen R (2019) A text sentiment classification modeling method based on coordinated cnn-lstm-attention model. Chinese Journal of Electronics 28(2019-1-120), 120 https://doi.org/10.1049/cje.2018.11.004

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