Toward Prompt-Enhanced Sentiment Analysis with Mutual Describable Information Between Aspects

Sentiment analysis aims to attain the sentiment polarity of the text, which is a coarse-grained approach and does not focus on the targets . On the other hand, an aspect-based sentiment analysis (ABSA) has recently gained boosting interest. The ABSA is a fine-grained sentiment assignment to determine the sentiment tendency toward a specific aspect. Most previous methods employ Recurrent Neural Network (RNN) coupled with attention mechanisms to accomplish this task. However, such RNN-based models tend to be complex and require much training. Recently, a growing number of BERT-style models have been emerging and presenting better results in ABSA tasks . Nevertheless, these methods cannot well distinguish the various logical relationships between aspects that exist in the data and thus do not model the relationship between aspects. In the manuscript, a prompt-enhanced sentiment analysis (PESA) is proposed. Hence, a novel and efficient approach could retrieve the training set that is most similar to the input text and represents the mutual information between aspects by utilizing the [MASK] token. Moreover, the proposed model only needs to forward once if a sentiment analysis of multiple aspects is required in the inference stage. The language representation model BERT is employed to boost the performance of the proposed method. Comprehensive experiments and conducted analysis indicate the efficiency of the proposed methodology.