Dataset and code for parper: Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification
- Python 3.8.3
- Pytorch 1.6.0
- Huggingface transformers 4.5.1
For Training model for each dataset:
sh run.shIf you want to get the trained models, please contact me by email.
For MAMS dataset, please refer to https://github.com/siat-nlp/MAMS-for-ABSA
If this work is helpful, please cite as:
@inproceedings{niu-etal-2022-composition,
title = "Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification",
author = "Niu, Hao and
Xiong, Yun and
Gao, Jian and
Miao, Zhongchen and
Wang, Xiaosu and
Ren, Hongrun and
Zhang, Yao and
Zhu, Yangyong",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.594",
pages = "6827--6836",
abstract = "Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity, which neglects the sentiment relationship among aspect terms. Besides, most current ABSA methods focus on sentences containing only one aspect term or multiple aspect terms with the same sentiment polarity, which makes ABSA degenerate into sentence-level sentiment analysis. In this paper, to deal with this problem, we construct a heterogeneous graph to model inter-aspect relationships and aspect-context relationships simultaneously and propose a novel Composition-based Heterogeneous Graph Multi-channel Attention Network (CHGMAN) to encode the constructed heterogeneous graph. Meanwhile, we conduct extensive experiments on three datasets: MAMSATSA, Rest14, and Laptop14, experimental results show the effectiveness of our method.",
}