摘要
随着微博用户数量的快速增长,微博中所携带的一些情感和观点对社会的影响越来越大,尤其是一些涉及到公众人身安全的负面情绪,可能会影响到社会的稳定,因此进行微博情感分析意义重大。微博情感分析的内容包括微博语料的获取、微博语料的预处理和情感分析方法等,常用的情感分析方法有基于情感词典的方法、基于机器学习的方法和基于深度学习的方法。随着注意力机制在NLP领域的广泛使用,很多研究者开始将注意力机制融合到深度学习模型中进行情感分析,这使得情感分析的准确率得到了很大的提升。谷歌提出的BERT模型本质上也是基于注意力机制实现的,BERT模型在情感分析领域取得了突破性的进展。
With the rapid growth of the number of microblog users,some emotions and opinions carried in microblog have a growing impact on the society,especially some negative emotions related to the personal safety of the public,which may affect the stability of the society.Therefore,it is of great significance to analyze the sentiment of microblog.The content of microblog sentiment analysis includes the acquisition of microblog corpus,the preprocessing of microblog corpus and the methods of sentiment analysis.The commonly used sentiment analysis methods include the method based on emotion dictionary,the method based on machine learning,and the method based on depth learning.With the widespread use of attention mechanism in NLP field,many researchers began to integrate attention mechanism into deep learning model for sentiment analysis,which greatly improves the accuracy of sentiment analysis.The BERT model proposed by Google is also based on attention mechanism essentially,which has made a breakthrough in the field of sentiment analysis.
作者
王春东
张卉
莫秀良
杨文军
WANG Chun-dong;ZHANG Hui;MO Xiu-liang;YANG Wen-jun(School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384,China)
出处
《计算机工程与科学》
CSCD
北大核心
2022年第1期165-175,共11页
Computer Engineering & Science
基金
国家自然科学基金(U1536122)
天津市科学技术委员会基金(15JCYBJC15600)。
关键词
微博舆论
情感分析
深度学习
注意力机制
BERT
public opinion on microblog
sentiment analysis
deep learning
attention mechanism
BERT
