Abstract: In the realm of social media,facial expressions of characters in pictures often captivate our attention first,directly evoking strong emotional responses.However,for a truly comprehensive emotional expression,scenes play a pivotal role,serving as a crucial backdrop and support for emotional analysis.Scenes provide context,setting the tone and atmosphere for the emotions being expressed.Regrettably,numerous scholars have failed to fully recognize the significance of scenes in emotional expression,often focusing solely on facial expressions.This oversight has led to suboptimal outcomes in sentiment analysis,missing out on the rich emotional nuances that scenes can provide.To address these challenges,we propose the multi-view image text sentiment analysis network(MITN).This innovative approach takes into account both facial expressions and scenes,providing a more comprehensive analysis of emotional expression.In MITN,we enhance image feature extraction by incorporating an attention mechanism that meticulously captures the facial expressions of characters.At the same time,dilated convolution is introduced to broa-den the receptive field,focusing on the intricate details of the scene.Moreover,we leverage the Places dataset for transfer learning training of Scene-VGG.This allows us to fully utilize the vast amount of scene information available,enhancing the accuracy and depth of our emotional analysis.The effectiveness of MITN is rigorously tested through experiments on the multimodal sentiment dataset MVSA.Utilizing BERT+BiGRU to extract text expression features,our model demonstrates superior performance in sentiment analysis,accurately capturing the emotional nuances present in both facial expressions and scenes.This comprehensive approach offers a new perspective in sentiment analysis,paving the way for more accurate and nuanced understanding of emotio-nal expression in social media.

Key words: Multi-modal, Sentiment analysis, Multi-view, Transfer learning, Attention mechanism