A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction

Aspect-based sentiment analysis (ABSA) task is a fine-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the related works merely focus on the subtask of Chinese aspect term polarity inferring and fail to emphasize the research of Chinese-oriented ABSA multi-task learning. 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 other models, this model equips the capability of extracting aspect term and inferring aspect term polarity synchronously. The experimental results on four Chinese review datasets outperform state-of-the-art performance on the ATE and APC subtask. And by integrating the domain-adapted BERT model, LCF-ATEPC achieves the state-of-the-art performance of ATE and APC in the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets. Moreover, this model is effective to analyze both Chinese and English reviews collaboratively and the experimental results on a multilingual mixed dataset prove its effectiveness. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a multi-task learning model LCF-ATEPC for Chinese-oriented aspect-based sentiment analysis, which is capable of extracting aspect terms and inferring their polarity simultaneously. The experimental results demonstrate its superior performance on Chinese review datasets and achieve state-of-the-art performance on SemEval-2014 task4 Restaurant and Laptop datasets. Moreover, it is effective in analyzing both Chinese and English reviews collaboratively and shows good results on a multilingual mixed dataset.