@inproceedings{fan2022sentiment-aware,
author = {Fan, Shuai and Lin, Chen and Li, Haonan and Lin, Zhenghao and Su, Jinsong and Zhang, Hang and Gong, Yeyun and Guo, Jian and Duan, Nan},
title = {Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis},
booktitle = {EMNLP 2022},
year = {2022},
month = {October},
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 this https URL.},
url = {http://approjects.co.za/?big=en-us/research/publication/sentiment-aware-word-and-sentence-level-pre-training-for-sentiment-analysis/},
}