Multiview Sentiment Analysis of Social Media Based on Feature Fusion
Modern social media gives everyone the opportunity for diverse expressions,and sentiment analysis based on social media can identify the current emotional state of users.However,most of the existing sentiment analysis methods use a single modal-ity for analysis,which leads to inaccurate sentiment state identification.To solve this problem,this paper proposes a multimodal sentiment classification model based on feature fusion.First,text sentiment features are extracted using the BERT model,then im-age sentiment features are extracted using resNet152,the traditional stitching structure is replaced by using the"+"structure,and the residual network is used to fuse features with higher quality.Finally,the impact of the sentiment relevance of the image text on the sentiment analysis is explored.The experimental results show that the proposed method can significantly improve the sentiment recognition accuracy on the MVSA-simple and MVSA-multi public datasets.The proposed method in this paper opens a new path-way for multimodal social media sentiment analysis.
sentiment analysisneutral networktext and imagesocial networkingresidual network
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