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

• 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. Aspect level sentiment classification is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of one or more given aspects in a sentence. In natural language, words frequently carry certain sentimental tendencies, which can be beneficial in obtaining the features between aspects and contexts. On the other hand, the dependencies between different aspects in a sentence can provide sufficient information for the sentiment polarity discrimination of a target aspect. However, existing models tend to focus on sentiment knowledge or aspect interactions individually without leveraging their converged information. Therefore, we propose a model based on G ated M echanism F using S entiment K nowledge and I nter- A spect dependency (GMF-SKIA) for Aspect-level Sentiment Classification in this paper, aiming to dynamically fuse sentiment knowledge information of words and inter-aspect dependency. Specifically, the model uses the SenticNet sentiment dictionary to add sentiment knowledge information to words during dependency tree construction, and then we introduce a graph convolutional network to obtain sentiment information of dependency tree. We utilize an aspect-related multiheaded self-attention mechanism to model the inter-aspect interactions. Moreover, we design an information gate based on gated mechanism to fuse sentiment knowledge and inter-aspect features. We performed experiments on four publicly available datasets, 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. [ABSTRACT FROM AUTHOR]