摘要
为帮助政府等相关部门及时掌握大众对特定公共事件的主要情感倾向,针对基于词向量的深度学习方法实现网民情绪识别,存在高度依赖分词准确性、一词多义等问题,提出基于BERT-BiLSTM的网民情绪识别方法。首先,基于BERT预训练模型获取预处理后的待识别文本词向量;然后,利用BiLSTM提取上下文相关特征进行学习;最后,通过分类器获得文本的情感极性,包括积极和消极两类。通过对疫情期间网民情绪识别数据集实验表明,基于BERT-BiLSTM的网民情绪识别模型P值为88.98%,R值为92.72%,F1值为90.81%,相比于LSTM和BiLSTM模型性能更优。本识别方法可为网民情绪识别研究提供借鉴,识别结果可为政府决策分析和舆情引导提供参考。
In order to help the government and other relevant departments grasp the main emotional tendencies of the public on spe⁃cific public events in time,aiming at the problems of high dependence on word segmentation accuracy and polysemy in deep learn⁃ing method based on word vector to realize internet users emotion recognition,this paper proposes ainternet users emotion recogni⁃tion method based on BERT-BiLSTM.Firstly,the text word vector with preprocessed and to be recognized is obtained based on the BERT;secondly,the context sensitive features are extracted by the BiLSTM for learning;finally,the emotional polarity of the text,including positive and negative,is obtained through the classifier.The experiments of internet users emotion recognition data set during the epidemic period shows that the Precision is 88.98%,Recall is 92.72%,and F1 is 90.81%based on BERT-BiLSTM,which is better than LSTM and BiLSTM.The recognition method can provide reference for the research of internet users emotion recognition,and the recognition results can provide reference for government decision-making analysis and public opinion guid⁃ance.
作者
潘梅
PAN Mei(Chengdu Normal University,Chengdu 611130,China)
出处
《电脑知识与技术》
2021年第18期74-76,共3页
Computer Knowledge and Technology
基金
成都师范学院校级科研项目(CS20ZC06)。
