Auto-ABSA: Cross-Domain Aspect Detection and Sentiment Analysis Using Auxiliary Sentences
This work addresses aspect-based sentiment analysis for text analysis applications, but it is incremental as it builds on existing transformer-based methods with a specific auxiliary approach.
The paper tackles cross-domain aspect-based sentiment analysis by using auxiliary sentences for aspect detection to aid sentiment prediction, and demonstrates that their method outperforms baselines that use no aspects or all aspects.
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.