跨领域文本的可迁移情绪分析方法

随着移动互联网的迅猛发展,社交网络平台充斥着大量带有情绪色彩的文本数据,对此类文本中的情绪进行分析研究不仅有助于了解网民的态度和情感,而且对科研机构和政府掌握社会的情绪变化及走向有着重要作用。传统的情感分析主要对情感倾向进行分析,无法精确、多维度地描述出文本的情绪,为了解决这个问题,文中对文本的情绪分析进行研究。首先针对不同领域文本数据集中情绪标签缺乏的问题,提出了一个基于深度学习的可迁移情绪分类的情感分析模型FMRo-BLA,该模型对通用领域文本进行预训练,然后通过基于参数的迁移学习、特征融合和FGM对抗学习,将预训练模型应用于特定领域的下游情感分析任务中,最后在微博的公开数据集上进行对比实验。结果表明,该方法相比于目前性能最好的RoBERTa预训练语言模型,在目标领域数据集上F1值有5.93%的提升,进一步加入迁移学习后F1值有12.38%的提升。

Transferable Emotion Analysis Method for Cross-domain Text

With the rapid development of mobile internet,social network platform is full of a large number of text data with emotional color.Mining the emotion in such text not only helps to understand the attitude and emotion of internet users,but also plays an important role in scientific research institutions and the government to grasp the emotional changes and trends of society.Traditional sentiment analysis mainly focuses on the analysis of sentiment tendency,which can not accurately and multi-dimensionally describe the emotion of the text.In order to solve this problem,this paper studies the emotion analysis of the text.Firstly,aiming at the lack of fine-grained sentiment tags in text data sets of different fields,a deep learning based emotion analysis model,FMRo-BLA,is proposed.The model pre-trains the general domain text,and then applies the pre-trained model to the downstream situation of specific domain through parameter based migration learning,feature fusion and FGM Adversarial training.Compared with the best performance of RoBERTa pre-trained language model,the F1value of the proposed method is improved by 5.93%on the target domain dataset,and it achieves 12.38%by further adding transfer learning.