Fusing sentiment knowledge and inter-aspect dependency based on gated mechanism for aspect-level sentiment classification

Yu Han, Xiaotang Zhou, Guishen Wang, Yuncong Feng, Hui Zhao, Junhua Wang

Abstract: Highlights•We believe that integrating sentiment knowledge and aspects interaction is beneficial to help the model better understand the interactions of different aspects in sentences and the sentiment relations between words, which is ignored by the existing research.•We propose a model named GMF-SKIA. We first add sentiment knowledge information to word nodes through SenticNet. After that, we use an aspect-related self-attention mechanism to obtain inter-aspect feature information. Lastly, we use an information gate to dynamically fuse information.•We evaluate GMF-SKIA on four datasets, Rest14, Lap14, Rest15 and Rest16, our model outperforms the best benchmark model by an average of 2.1% and achieves the highest accuracy of 91.56% on the Rest16 dataset.