A multi-scale sarcasm sentiment recognition algorithm incorporating sentence hierarchical representation

Sarcasm is a special kind of linguistic sentiment that is widely used in a wide range of social media to express strong emotions in users. Therefore, the task of sarcasm recognition is particularly important for social media analysis. There are few studies on sarcasm sentiment recognition in Chinese, and they often ignore the complex interactions between different syntactic components of a sentence, such as sentiment words, entities, dummy words, and special punctuation that occur in the text. In order to improve the accuracy of Chinese sarcasm recognition, this paper proposes a multi-scale neural network sarcasm recognition algorithm incorporating a hierarchical representation of sentences, taking into account the semantic information of sentences and the relationship features between different syntactic components. The hierarchical syntactic tree is reconstructed to distinguish the key components of the sentence, and the multi-channel convolutional network is used to mine the relational features between syntactic levels and deeply fuse them with semantic information to perform the Chinese sarcastic sentiment recognition task. We have tested the method on a publicly available Chinese sarcastic comment dataset, and the results show that the method can effectively improve the accuracy rate of Chinese sarcastic sentiment recognition.