Following the proposal of the transformer, many pre-trained language models were developed, and the sentiment analysis (SA) task was improved.In this paper, we proposed a method that uses an auxiliary sentence to describe aspects that the sentence contains to help sentiment prediction. The ﬁrst is aspect detection, which uses a multi-aspects detection model to predict all the aspects that the sentence has. Combining the predicted aspects and the original sentence as the Sentiment Analysis (SA) model’s input. The second is to do out-of-domain aspect-based sentiment analysis (ABSA), train a sentiment classiﬁcation model with 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 the sentiment classiﬁcation model’s input, respectively. Compared two baselines’ performance to our method, we found that our method really makes sense.