  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.
