多维度注意力机制下网络舆情视觉情感识别模型及识别效果研究

Data Analysis and Knowledge Discovery Research on Recognition Model and Recognition Effect of Network Public Opinion Visual Emotion under Multi-Dimensional Attention Mechanism

【目的】为弥补当前视觉情感分析研究的不足,构建基于ResNet34改进的情感分析模型,分析和提高图像情感分类的精度。【方法】首先基于ResNet34架构建立视觉情感识别模型,然后通过融合CBAM模块和Non-Local模块,对情感特征进行学习、表示,最后利用以上模型对情感特征进行分类识别,并且与VGG16和ResNet50模型进行对比以验证构建模型的优越性及精度。【结果】通过实验验证所构建的模型的识别效果,研究结果表明模型的准确率、精确率、召回率和F1值分别达到84.42%、84.10%、83.70%和83.80%。与基线模型进行对比,所提模型的准确率相比于VGG16和ResNet50模型分别提升4.17和3.44个百分点,F1值分别提升4.20和3.30个百分点。【局限】测试的数据集规模相对不大,未采用皮尔曼系数等计算标注的效果,未将基于视觉的情感分类算法进行比较。【结论】从视觉情感分析视角对情感识别模型进行优化,补充了情感计算的分析模态,为舆情信息情感特征提取和分析提供了支撑。

[Objective]To fill the current deficiency in visual emotional analysis research,a ResNet34-based improved emotion analysis model was constructed to analyze and improve the accuracy of image emotion classification.[Methods]Firstly,a visual emotion recognition model was established based on the ResNet34.Then,by integrating the CBAM module and Non-Local module,emotion features were learned and represented.Finally,the above model was used to classify and recognize emotional features,and compared with VGG16 and ResNet50 models.[Results]The recognition effect of the constructed model was verified through experiments,and the research results showed that the accuracy,precision,recall,and F1 score of the model reached 84.42%,84.10%,83.70%,and 83.80% respectively.Compared with the baseline models of the VGG16 and ResNet50,the accuracy of the proposed model was improved by 4.17% and 3.44%,and the F1 score was improved by 4.20% and 3.30%.[Limitations]The scale of the test dataset is relatively small,the effectiveness of annotation was not calculated using metrics such as the Pearson correlation coefficient,and a comparison was not made with visualbased emotion classification algorithms.[Conclusions]From the perspective of visual emotional analysis,optimizing the emotion recognition model supplements the analysis mode of emotional computation,providing support for the extraction and analysis of emotional features in public opinion information.