Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

The fine-grained task of emotion analysis, emotion cause extraction, is a current research hotspot. It aims to discover the underlying reasons behind the emotional expression in texts. Most of the existing work regards the task as an independent text clause classification problem, ignoring the relationship between the clauses and failing to use the indicative relationship between emotional sentences and emotional cause sentences. The existence of these problems greatly affects the accuracy of the task. In this work, an emotion cause extraction method based on a hierarchical network emotional assistance mechanism is proposed. This method uses a hierarchical network composed of bidirectional gated recurrent units, attention mechanism, and graph convolutional networks to capture clause context information, deep semantic information, and structural information between clause neighborhoods. At the same time, by enhancing the emotional information representation of the graph convolutional network nodes, the clause features of the text emotional keywords are introduced into the discovery of candidate cause sentences. Thus, a model of the deep neural network combined with the emotional assistance mechanism is established. Compared with the existing methods, the model established in this paper has better classification performance on the Chinese emotion cause dataset.