基于方面词感知的多模态细粒度情感分析OA

The existing multimodal fine-grained sentiment analysis models have problems of insufficient extraction of textual and vi-sual features and neglect of guiding role of aspect during modality fusion.To address these issues,an aspect percept multimodal fine-grained sentiment analysis model was proposed.Firstly,a semantic alignment module was designed to capture aspect clues in imag-es,enhancing aspect awareness.Secondly,an aspect-oriented syntactic dependency graph and an emotion syntactic graph attention network were constructed to explore complex dependency relationships related to aspect in the text from multiple perspectives.Thirdly,a multi-feature encoding module was developed to extract rich visual feature clues.Finally,a dual cross-attention mecha-nism was introduced to obtain bidirectional interaction information between text and image modalities.The quality of aspect features was further improved using a gated semantic graph convolutional network and an aspect masking mechanism,while an attention mechanism was employed for aspect percept modality fusion.The experimental results demonstrated that,compared to baseline models,the proposed model achieved average accuracy improvements of 3.00 and 2.78 percentage points on the Twitter-2015 and Twitter-2017 public datasets,respectively,along with average Macro-F1 score increases of 3.50 and 2.53 percentage points.These findings confirmed the model's effectiveness in enhancing the performance of multimodal fine-grained sentiment analysis.