@inproceedings{zhang-etal-2023-empirical,
title = "An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis",
author = "Zhang, Yice and
Yang, Yifan and
Liang, Bin and
Chen, Shiwei and
Qin, Bing and
Xu, Ruifeng",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.612/",
doi = "10.18653/v1/2023.findings-acl.612",
pages = "9633--9651",
abstract = "Aspect-Based Sentiment Analysis (ABSA) aims to recognize fine-grained opinions and sentiments of users, which is an important problem in sentiment analysis. Recent work has shown that Sentiment-enhanced Pre-Training (SPT) can substantially improve the performance of various ABSA tasks. However, there is currently a lack of comprehensive evaluation and fair comparison of existing SPT approaches. Therefore, this paper performs an empirical study to investigate the effectiveness of different SPT approaches. First, we develop an effective knowledge-mining method and leverage it to build a large-scale knowledge-annotated SPT corpus. Second, we systematically analyze the impact of integrating sentiment knowledge and other linguistic knowledge in pre-training. For each type of sentiment knowledge, we also examine and compare multiple integration methods. Finally, we conduct extensive experiments on a wide range of ABSA tasks to see how much SPT can facilitate the understanding of aspect-level sentiments."
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%0 Conference Proceedings
%T An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis
%A Zhang, Yice
%A Yang, Yifan
%A Liang, Bin
%A Chen, Shiwei
%A Qin, Bing
%A Xu, Ruifeng
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-empirical
%X Aspect-Based Sentiment Analysis (ABSA) aims to recognize fine-grained opinions and sentiments of users, which is an important problem in sentiment analysis. Recent work has shown that Sentiment-enhanced Pre-Training (SPT) can substantially improve the performance of various ABSA tasks. However, there is currently a lack of comprehensive evaluation and fair comparison of existing SPT approaches. Therefore, this paper performs an empirical study to investigate the effectiveness of different SPT approaches. First, we develop an effective knowledge-mining method and leverage it to build a large-scale knowledge-annotated SPT corpus. Second, we systematically analyze the impact of integrating sentiment knowledge and other linguistic knowledge in pre-training. For each type of sentiment knowledge, we also examine and compare multiple integration methods. Finally, we conduct extensive experiments on a wide range of ABSA tasks to see how much SPT can facilitate the understanding of aspect-level sentiments.
%R 10.18653/v1/2023.findings-acl.612
%U https://aclanthology.org/2023.findings-acl.612/
%U https://doi.org/10.18653/v1/2023.findings-acl.612
%P 9633-9651
Markdown (Informal)
[An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis](https://aclanthology.org/2023.findings-acl.612/) (Zhang et al., Findings 2023)
ACL