SENER: Sentiment Element Named Entity Recognition for Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) is a task of identifying fine-grained sentiment entities in a given sentence, which is generally formulated as a sequence labeling problem. Recently, advancements in large pre-trained language models (PLMs) led to generative ABSA, where the task is treated as text-to-text transition resolved by fine-tuning PLMs. Although the generative methods are designed to capture sentence-level semantic information, they are inappropriate for explicit comprehension of sentiment structure. In order to address this issue, we propose sentiment element named entity recognition (SENER) for ABSA. SENER integrates the concepts of named entity recognition (NER) and generative ABSA to retrieve the sentiment entities with predefined sentiment element names, leading to better semantic and sentiment structure understanding. Experimental results on several ABSA tasks show that the proposed SENER significantly outperforms previous works on ASQP and ASTE.