PIN: A Prompt-based Implicit Sentiment Analysis Network for Chinese

Abstract

For sentiment analysis (SA) issue, most current SA models focus on Explicit Sentiment Analysis (ESA), with less attention to Implicit Sentiment Analysis (ISA). Recent ISA models cannot fully consider prior knowledge in Pre-trained Language Model (PLM) for this knowledge intensive task, even introducing complex external knowledge bases. This also leads to limited performance with additional computational resources. To address these problems, we propose a Prompt-based Implicit sentiment analysis Network (PIN) for Chinese ISA, where a topic recognition module is introduced to identify the topic of the review. Then, the topic is embedded in the soft template to predict sentiment based on prompt learning, which can effectively activate PLM knowledge with low computational resources. Experiments conducted on three public datasets demonstrate the effectiveness of our model, as compared with state-of-the-art methods. Meanwhile, we also provide our identified topic as a supplement to the above datasets, forming three new datasets.