  After transformer is proposed, lots of pre-trained language models have been
come 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 the
sentence contains to help sentiment prediction. The first is aspect detection,
which uses a multi-aspects detection model to predict all aspects that the
sentence has. Combining the predicted aspects and the original sentence as
Sentiment Analysis (SA) model's input. The second is to do out-of-domain
aspect-based sentiment analysis(ABSA), train 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 sentiment
classification model's input, respectively. Compare two baselines performance
to our method, found that our method really makes sense.
