摘要
【目的/意义】微博情感分析对公共安全事件管控有着重要意义。现有研究将单条微博作为整体进行分析,情感分析最小单元局限于字或词,而对微博从词到句子,从句子到单条微博这种多层粒度文本结构产生的影响关注不足,基于此本文提出一种融合双层注意力的Bi-LSTM模型提升情感分析性能。【方法/过程】以红黄蓝幼儿园涉嫌虐童事件为例,通过Bi-LSTM提取微博词级和句子级特征,结合双层注意力机制学习各级特征权重分布,以递进顺序综合局部情感得到整条微博的情感分类。【结果/结论】实验结果表明,本研究提出的微博情感分析模型F1值、准确率分别达到97.39%、97.62%,相比于SVM、RF、XGBOOST和LSTM,该模型能够在公共安全事件微博情感分析方面取得较好效果。
【Purpose/significance】The micro-blog sentiment analysis is of great significance for the management and control of public safety events.Previous studies often analyze a single micro-blog as a whole and the minimum unit of sentiment analysis is limited to words,but insufficient attention is paid to the influence of the structure of the micro-blog text that from words to sentences and from sentence to single micro-blog.In order to solve this problem and improve analytical performance,a bidirectional long short term memory model with dual-layer attention is proposed.【Method/process】Taking the incident of child abuse in the Red,Yellow and Blue kindergarten as an example,the micro-blog word level and sentence level features are extracted through Bi-LSTM,and the dual-layer attention mechanism is used to learn the weight distribution of characteristics of each level,then integrate local sentiments in a progression order to get the sentimental classification results of the whole micro-blog.【Result/conclusion】The experimental results show that the F1-score and accuracy of the micro-blog sentiment analysis model proposed in this study reaches 97.39%and 97.62%.Compared with SVM,RF,XGBOOST and LSTM,better results can be achieved in this micro-blog sentiment analysis model of public security events.
作者
曾子明
万品玉
ZENG Zi-ming;WAN Pin-yu(Center for Studies of Information Resources,Wuhan University,Wuhan 430072,China)
出处
《情报科学》
CSSCI
北大核心
2019年第6期23-29,共7页
Information Science
基金
教育部人文社会科学重点研究基地重大项目"大数据资源的智能化管理与跨部门交互研究--面向公共安全领域"(16JJD870003)
