  Aspect-based sentiment analysis (ABSA) task is a multi-grained task of
natural language processing and consists of two subtasks: aspect term
extraction (ATE) and aspect polarity classification (APC). Most of the existing
work focuses on the subtask of aspect term polarity inferring and ignores the
significance of aspect term extraction. Besides, the existing researches do not
pay attention to the research of the Chinese-oriented ABSA task. Based on the
local context focus (LCF) mechanism, this paper firstly proposes a multi-task
learning model for Chinese-oriented aspect-based sentiment analysis, namely
LCF-ATEPC. Compared with existing models, this model equips the capability of
extracting aspect term and inferring aspect term polarity synchronously,
moreover, this model is effective to analyze both Chinese and English comments
simultaneously and the experiment on a multilingual mixed dataset proved its
availability. By integrating the domain-adapted BERT model, the LCF-ATEPC model
achieved the state-of-the-art performance of aspect term extraction and aspect
polarity classification in four Chinese review datasets. Besides, the
experimental results on the most commonly used SemEval-2014 task4 Restaurant
and Laptop datasets outperform the state-of-the-art performance on the ATE and
APC subtask.
