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Irony is the use of counterwords, irony and other techniques to reflect the essential characteristics of people and things, and is a way to express emotions. Irony detection is an important research content in the field of sentiment analysis, and it is also one of the hot issues in the research of artificial intelligence in natural language processing. In this paper, a sentimental ironic detection model based on multi-feature fusion is proposed, which takes text features, image features and image attribute features as inputs, and image attribute features are represented by adjective-noun pairs abbreviated as ANPs. In this model, the neural network is first used to extract features from the three inputs, and then the text features and image features are fused through the shared fusion network, and the contrast fusion network fuses the text features and image attribute features, so as to express the contrast and difference of graphic and text information. Finally, satirical classification is carried out through the classification layer. Experimental results show that the model achieves good performance on the public multimodal irony detection dataset.
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