ML Anthology

Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)

Haopeng Ren, Yi Cai, Yushi Zeng
AAAI 2022 pp. 13033-13034
doi:10.1609/AAAI.V36I11.21653 /aaai/2022/ren2022aaai-aspect/

Abstract

Cross-domain sentiment analysis (SA) has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, exiting unsupervised cross-domain SA models ignore the relation between the aspect and opinion, which suffer from the sentiment transfer error problem. To solve this problem, we propose an aspect-opinion sentiment alignment SA model and extensive experiments are conducted to evaluate the effectiveness of our model.

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Cite

Text

Ren et al. "Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21653

Markdown

[Ren et al. "Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/ren2022aaai-aspect/) doi:10.1609/AAAI.V36I11.21653

BibTeX

@inproceedings{ren2022aaai-aspect,
  title     = {{Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)}},
  author    = {Ren, Haopeng and Cai, Yi and Zeng, Yushi},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13033-13034},
  doi       = {10.1609/AAAI.V36I11.21653},
  url       = {https://mlanthology.org/aaai/2022/ren2022aaai-aspect/}
}