Analysis Model for Chinese Implicit Sentiment Based on Text Graph Representation

[Objective] This paper proposes a Chinese implicit sentiment analysis model based on text graph representation. It fully utilizes external knowledge and context to enhance implicit sentiment text and achieve word-level semantic interaction. [Methods] First, we modeled the target sentence and context as a text graph with words as nodes. Then, we obtained the semantic expansion of the word nodes in the graph through external knowledge linking. Finally, we used the Graph Attention Network to transfer semantic information between the nodes of this text graph. We also obtained the text graph representation through the Readout function. [Results] We evaluated the model on the publicly available implicit sentiment analysis dataset SMP2019-ECISA. Its F1 score reached 78.8%, at least 1.2% higher than the existing model. [Limitations] The size of the generated text graph is related to the length of the text, leading to significant memory and computational overhead for processing long text. [Conclusions] The proposed model uses graph structure to model the relationship between external knowledge, context, and the target sentence at the word level. It effectively represents text semantics and enhances the accuracy of implicit sentiment analysis. © 2024 Chinese Academy of Sciences. All rights reserved.