To detect multiple coexisting emotions from public emergency opinions, this article proposes a novel two-stage multiple coexisting emotion-detection model. First, the text semantic feature extracted through bidirectional encoder representation from transformers (BERT) and the emotion lexicon feature extracted through the emotion dictionary are fused. Then, the emotion subjectivity judgement and multiple coexisting emotion detection are performed in two separate stages. In the first stage, we introduce synthetic minority oversampling technique (SMOTE) to enhance the balance of data distribution and select the optimal classifier to recognise opinion texts with emotion. In the second stage, the label powerset (LP)-SMOTE is proposed to increase the number of the minority category samples, and multichannel emotion classifiers and the decision mechanism are employed to recognise different types of emotions and determine the final coexisting emotion labels. Finally, the Weibo data about coronavirus disease 2019 (COVID-19) are collected to verify the effectiveness of the proposed model. Experiment results indicate that the proposed model outperforms state-of-the-art models, with the F1_macro of 0.8532, the F1_micro of 0.8333, and the hamming loss of 0.0476. The emotion detection results are conducive to decision-making for public emergency departments.

中文翻译：

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从突发公共事件意见中检测多种共存情绪

为了从突发公共事件意见中检测多种共存情绪，本文提出了一种新颖的两阶段多种共存情绪检测模型。首先，融合通过双向编码器表示变换器（BERT）提取的文本语义特征和通过情感词典提取的情感词典特征。然后，情感主观性判断和多种共存情感检测分两个阶段进行。在第一阶段，我们引入合成少数过采样技术（SMOTE）来增强数据分布的平衡，并选择最佳分类器来识别带有情感的意见文本。第二阶段，提出标签幂集（LP）-SMOTE来增加少数类别样本的数量，并采用多通道情感分类器和决策机制来识别不同类型的情感并确定最终共存的情感标签。最后，收集有关2019冠状病毒病（COVID-19）的微博数据来验证所提出模型的有效性。实验结果表明，所提出的模型优于最先进的模型，F1_macro为0.8532，F1_micro为0.8333，汉明损失为0.0476。情绪检测结果有利于公共应急部门决策。