Mitigating unreliable syntactic dependencies in aspect-based sentiment analysis via sentiment-aware graph anomaly detection and mix convolution.
- DOI
- 10.1016/j.neunet.2026.109490
- Published
- 2026 Aug 18
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109490,
title = {Mitigating unreliable syntactic dependencies in aspect-based sentiment analysis via sentiment-aware graph anomaly detection and mix convolution.},
author = {Wang J and Cui Z and Gao H and Li Y and Jiang Y},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109490},
url = {https://doi.org/10.1016/j.neunet.2026.109490}
}RIS
TY - JOUR TI - Mitigating unreliable syntactic dependencies in aspect-based sentiment analysis via sentiment-aware graph anomaly detection and mix convolution. AU - Wang J AU - Cui Z AU - Gao H AU - Li Y AU - Jiang Y PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109490 UR - https://doi.org/10.1016/j.neunet.2026.109490 ER -
APA
J, W., Z, C., H, G., Y, L., & Y, J. (2026). Mitigating unreliable syntactic dependencies in aspect-based sentiment analysis via sentiment-aware graph anomaly detection and mix convolution.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109490
Source records
- pubmed · retrieved 2026-09-26T00:37:12.833Z