Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis

Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan

Abstract
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose SentiWSP, a novel Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. The word level pre-training task detects replaced sentiment words, via a generator-discriminator framework, to enhance the PLM’s knowledge about sentiment words. The sentence level pre-training task further strengthens the discriminator via a contrastive learning framework, with similar sentences as negative samples, to encode sentiments in a sentence. Extensive experimental results show that SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks. We have made our code and model publicly available at https://github.com/XMUDM/SentiWSP.
Anthology ID:
2022.emnlp-main.332
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4984–4994
Language:
URL:
https://aclanthology.org/2022.emnlp-main.332
DOI:
10.18653/v1/2022.emnlp-main.332
Bibkey:
Cite (ACL):
Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, and Nan Duan. 2022. Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4984–4994, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (Fan et al., EMNLP 2022)
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https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.332.pdf
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