Previous studies show effective of pre-trained language models for sentiment analysis. However, most of these studies ignore the importance of sentimental information for pre-trained models.Therefore, we fully investigate the sentimental information for pre-trained models and enhance pre-trained language models with semantic graphs for sentiment analysis.In particular, we introduce Semantic Graphs based Pre-training(SGPT) using semantic graphs to obtain synonym knowledge for aspect-sentiment pairs and similar aspect/sentiment terms.We then optimize the pre-trained language model with the semantic graphs.Empirical studies on several downstream tasks show that proposed model outperforms strong pre-trained baselines. The results also show the effectiveness of proposed semantic graphs for pre-trained model.
@article{arxiv.2105.12305,
title = {SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis},
author = {Yong Qian and Zhongqing Wang and Rong Xiao and Chen Chen and Haihong Tang},
journal= {arXiv preprint arXiv:2105.12305},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:2005.05635 by other authors