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

TL;DR

This paper introduces Auto-ABSA, a method that leverages auxiliary sentences about aspects to improve cross-domain aspect detection and sentiment analysis, demonstrating significant performance gains over baseline models.

Contribution

The paper proposes a novel approach using auxiliary aspect sentences to enhance cross-domain aspect detection and sentiment analysis, addressing domain transfer challenges.

Findings

Our method outperforms baseline models in cross-domain sentiment analysis.

Auxiliary aspect sentences significantly improve aspect detection accuracy.

The approach effectively generalizes across different datasets.

Abstract

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.