测试科学与仪器2021，Vol.12Issue(4)：479-488,10.DOI:10.3969/j.issn.1674-8042.2021.04.012
基于自注意力机制的弹幕文本情绪分类模型
Sentiment classification model for bullet screen based on self-attention mechanism
摘要
Abstract
With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.关键词
弹幕文本/文本情绪分类/自注意力机制/可视化分析/热点事件管控Key words
bullet screen/text sentiment classification/self-attention mechanism/visual analysis/hot events control引用本文复制引用
赵庶旭,刘李姣,马秦靖..基于自注意力机制的弹幕文本情绪分类模型[J].测试科学与仪器,2021,12(4):479-488,10.基金项目
National Natural Science Foundation of China(No.61562057) （No.61562057）
Gansu Science and Technology Plan Project(No.18JR3RA104) （No.18JR3RA104）
