Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (5): 179-185.DOI: 10.3778/j.issn.1002-8331.2009-0337

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Multiple Classification Method for Microblog Negative Emotions Integrating MAML and BiLSTM

XU Chao, YE Ning, XU Kang, WANG Ruchuan   

  1. 1.School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210000, China
    2.Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210000, China
  • Online:2022-03-01 Published:2022-03-01



  1. 1.南京邮电大学 计算机学院,南京 210000
    2.江苏省无线传感网高技术研究重点实验室,南京 210000

Abstract: With the continuous development of social networks, microblog has become an important platform for people to share their opinions and feelings in daily life. The analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion survey, commodity recommendation and other work. When faced with new tasks, the traditional deep learning algorithm often needs a large amount of data retraining to get a good accuracy. In view of this situation, this study proposes a microblog negative emotion multi-classification method based on MAML and BiLSTM. Firstly, the word vectioning of microblog text is quantified, and then the model combining MAML and BiLSTM is constructed. BiLSTM realizes the classification of negative emotions on microblog and updates the parameters through random gradient descent. The meta-learner in MAML updates its parameters by calculating the sum of the losses from multiple trainings and performing the second gradient descent. Finally, the learner obtained through the update can quickly iterate when faced with the new negative emotion classification task of microblog. The experimental results show that, compared with the current popular model, the accuracy, recall rate and F1-score of microblog negative emotion data set are improved by 1.68 percentage points, 2.86 percentage points and 2.27 percentage points, respectively.

Key words: bi-directional long short-term memory(BiLSTM), model-agnostic meta-learning(MAML), microblog, sentiment analysis

摘要: 随着社交网络的不断发展,微博成为人们日常生活中分享观点和感情的重要平台,分析用户的情感倾向可以有效地应用于舆情控制、民意调查、商品推荐等工作。传统的深度学习算法在面对新的工作任务时,往往需要大量数据重新训练才能得到较好准确率。针对这一情况,提出了一种基于MAML(model-agnostic meta-learning)与BiLSTM(双向长短时记忆网络)的微博负面情感多分类方法。对微博文本进行词向量化表示,构建MAML与BiLSTM结合的模型,其中BiLSTM实现对微博负面情感的分类,通过随机梯度下降更新参数;MAML中的元学习器则通过计算多次训练的损失总和,进行第二次梯度下降,更新元学习器参数。通过更新后得到的元学习器可以在面对新的微博负面情感分类任务时快速迭代。实验结果表明:相较于目前流行的模型,在微博负面情感数据集上,准确率、召回率和F1值分别提高了1.68个百分点、2.86个百分点和2.27个百分点。

关键词: 双向长短时记忆网络(BiLSTM), MAML, 微博, 情感分析