A Local and Global Context Focus Multilingual Learning Model for Aspect-Based Sentiment Analysis
Jiangtao He, Aishan Wumaier, Zaokere Kadeer, Weiwei Sun, Xiangzhe Xin, Linna Zheng
Abstract
Aspect-Based Sentiment Analysis (ABSA) aims to predict the sentiment polarity of different aspects in a sentence or document, which is a fine-grained task of natural language processing. Most of the existing work focuses on the correlation between aspect sentiment polarity and local context. The important deep correlations between global context and aspect sentiment polarity have not received enough attention. Besides, there are few studies on Chinese ABSA tasks and multilingual ABSA tasks. Based on the local context focus mechanism, we propose a multilingual learning model based on the interactive learning of local and global context focus, namely LGCF. Compared with the existing models, this model can effectively learn the correlation between local context and aspect words and the correlation between global context and aspect words simultaneously. In addition, the model can effectively analyze comments in Chinese and English simultaneously. Experiments conducted on three Chinese benchmark datasets(Camera, Phone and Car) and six English benchmark datasets(Laptop, Restaurant14, Restaurant16, Twitter, Tshirt and Television) demonstrate that LGCF has achieved compelling performance and efficiency improvements compared with several existing state-of-the-art models. Moreover, the ablation experiment results also verify the effectiveness of each part in LGCF.