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After transformer is proposed, lots of pre-trained language models have beencome up with and sentiment analysis (SA) task has been improved. In this paper,we proposed a method that uses an auxiliary sentence about aspects that thesentence contains to help sentiment prediction. The first is aspect detection,which uses a multi-aspects detection model to predict all aspects that thesentence has. Combining the predicted aspects and the original sentence asSentiment Analysis (SA) model's input. The second is to do out-of-domainaspect-based sentiment analysis(ABSA), train sentiment classification modelwith one kind of dataset and validate it with another kind of dataset. Finally,we created two baselines, they use no aspect and all aspects as sentimentclassification model's input, respectively. Compare two baselines performanceto our method, found that our method really makes sense.

Auto-ABSA: Cross-Domain Aspect Detection and Sentiment Analysis Using Auxiliary Sentences