随着基于互联网的社交媒体兴起,Emoji由于具有以图形化方式快速准确地表达情绪的特点,目前已经成为用户在日常交流中广泛使用的图像文本.已有研究工作表明,在基于文本的情绪识别模型中考虑 Emoj i 信息,对于提升模型性能具有重要的作用.目前,考虑 Emoj i 信息的情绪识别模型大多采用词嵌入模型学习 Emoj i 表示,得到的 Emoj i向量缺乏与目标情绪的直接关联,Emoji表示蕴含的情绪识别信息较少.针对上述问题,该文通过软标签为 Emoj i构建与目标情绪直接关联的情感分布向量,并将 Emoj i情感分布信息与基于预训练模型的文本语义信息相结合,提出融合Emoj i情感分布的多标签情绪识别方法(Emoji Emotion Distribution Information Fusion for Multi-label Emotion Recognition,EIFER).EIFER方法在经典的二元交叉熵损失函数的基础上,通过引入标签相关感知损失对情绪标签间存在的相关性进行建模,以提升模型的多标签情绪识别性能.EIFER方法的模型结构由语义信息模块、Emoji信息模块和多损失函数预测模块组成,采用端到端的方式对模型进行训练.在 SemEval2018 英文数据集上的情绪预测对比实验结果表明,该文提出的EIFER方法比已有的情绪识别方法具有更优的性能.
Emoji Emotion Distribution Information Fusion for Multi-Label Emotion Recognition
With the rise of Internet-based social media,Emoji has become a widely used image text for users in daily communication due to its graphical emotion expression.Existing studies on emotion recognition models simply con-vert the Emoji into word vectors,without directly capture its correlation with the target emotion.This paper propo-ses to construct an emotion distribution vector directly associated with the target emotion through soft labels,and to combine the Emoji emotion distribution information with text semantic information via the pre-training model,which is named EIFER(i.e.Emoji emotion distribution Information Fusion for multi-label Emotion Recognition).Based on the classical binary cross-entropy loss function,EIFER method models the correlation between emotion la-bels by introducing label-correlation aware loss.The EIFER method is an end-to-end model composed by a semantic information module,an Emoji information module and a multi-loss function prediction module.Experiment results on the SemEval2018 English dataset have shown that the proposed method has better performance than the existing methods.
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