[目的/意义]当前网络舆情事件中网民情感分析研究多聚焦于文本、文本结合图片和视频等内容，缺乏针
对图片的探讨。同时，视觉情感分析中多视觉语义特征融合缺乏相应的理论指导。[方法/过程]文章借鉴多模态
融合思想，以此作为多视觉语义特征融合的理论指导，按照特征层融合、中间层融合、决策层融合和混合融合策
略，以在 ImageNet 数据集中预训练的 VGG 19模型和 Xception 模型为基础，设计对应的网络舆情视觉情感分析模
型。[结果/结论]文章将提出的模型在网络舆情图片数据集中展开实证研究，并同基线模型做对比。实验结果表
明我们提出的基于决策层融合的网络舆情视觉情感分析模型表现最佳。为增强模型的可解释性，本文对网络舆
情视觉情感分析模型中卷积层的输出进行了可视化分析。

[Purpose/significance] Currently, researches on sentiment analysis of online users in online public opinion
events focus on texts, texts combined with images or videos, lacking the discussion on images. Additionally, there is a
lack on the visual sentiment analysis fusing multiple visual semantics features. [Method/process] We draw the idea
from multimodal fusionand employ the thought of multimodal fusion as the theory guidance of multiple visual semantics
feature fusion. We design four different visual sentiment analysis models based on feature-level fusion, intermediate fusion, decision-level fusion and hybrid fusion. The designed models are basedon VGG 19and Xception pretrained on
the ImageNet. [Result/conclusion] We conduct empirical experiments on image datasets in online public opinion
events and compare the proposed models with baseline models. Experiments show that the model based on decision-level fusion is superior. To hence the interpretability of the model, we visualize the outputs from the convolutional lay ?
ers in the visual sentiment analysis model.