Shuai Fan


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
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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