计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (01): 165-175.
王春东,张卉,莫秀良,杨文军
收稿日期:
2020-08-16
修回日期:
2020-10-27
接受日期:
2022-01-25
出版日期:
2022-01-25
发布日期:
2022-01-13
基金资助:
WANG Chun-dong,ZHANG Hui,MO Xiu-liang,YANG Wen-jun
Received:
2020-08-16
Revised:
2020-10-27
Accepted:
2022-01-25
Online:
2022-01-25
Published:
2022-01-13
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。
王春东, 张卉, 莫秀良, 杨文军. 微博情感分析综述[J]. 计算机工程与科学, 2022, 44(01): 165-175.
WANG Chun-dong, ZHANG Hui, MO Xiu-liang, YANG Wen-jun. Overview on sentiment analysis of microblog[J]. Computer Engineering & Science, 2022, 44(01): 165-175.
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