A survey on personalized document-level sentiment analysis

Personalized document-level sentiment analysis (PDSA) plays an important role in many real-world applications. So far, various deep learning models for PDSA have been proposed. However, there has been no systematic survey in this area. To address this issue, in this paper, we overview the existing methods of PDSA, and propose a novel two-dimensional PDSA taxonomy. Specifically, in the dimension of attribute usage type, PDSA works are divided into two groups: (1) user-based and (2) user and product-based models. In the dimension of attribute processing method, PDSA works are divided into two groups: (1) feature-based and (2) relation-based models. To fill in the research blank indicated by the taxonomy, we further propose an architecture named User Correlation Mining (UCM) for PDSA. Specifically, UCM contains two components, namely Similar User Cluster Module (SUCM) and Triple Attributes BERT Model (TABM). SUCM is responsible for user clustering, and TABM aims to classify the user's sentiment based on the information of users, products, user clusters and user reviews. To evaluate the performances of the existing works as well as UCM, we conduct extensive experiments on three real-world datasets. The experiment results show that our proposed architecture UCM outperforms the other state-of-the-art methods.