Fuzzy Humanized Sentiment Knowledge Enhanced Dependence Graphs Implementing With Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a challenging and attractive fine-grained subtask in the natural language processing community, which aims to predict the sentiment polarity of each involved aspect term in the given sentence. With the numerous efforts contributed by related researchers, impressive success has been achieved in employing the external knowledge to enhance the sentiment representation. However, in previous works, the utilization of external knowledge is quite straightforward, which does not match the sentiment characteristics of human beings. Thus, this work proposes a novel humanized ABSA method, namely, fuzzy humanized sentiment-assisted graph attention networks (FHSGAT). Preliminary, to acquire the essential semantic features, the sentence is reconstructed by the manually designed prompt template, which would assist the pretrained language model in generating the target-guided semantic representation. In addition, based on the normal dependence graph, triple-dimensional external knowledge is injected into the corresponding nodes. Specifically, this article leverages the Gaussian membership function to fuzzify the sentiment knowledge-enhanced nodes, enabling the networks to obtain the humanized sentiment relationship between graph nodes, namely, fuzzy humanized syntactic features. Besides, due to its exquisite performance in information fusion, graph attention networks are utilized to accomplish the integration of target-guided semantic representation and fuzzy humanized syntactic features synchronously. Eventually, to validate the effectiveness of the proposed method FHSGAT, extensive experiments are conducted on five publicly and widely used benchmarks, and the corresponding results show the performance of the proposed approach outperforms the state of the art obviously.