摘要
文本情感分析最基础且最关键的一个环节就是构建一个高质量情感词典.为克服传统的情感词典中词汇所表达出的情感倾向不变性等问题的不足,本文基于三层神经网络结构构建动态情感词典.第一层通过改进的CBOW神经网络提取含有情感信息的特征;第二层通过双向LSTM神经网络,利用二叉语义依存结构模型提取出二叉语义依存路径特征;第三层在前两层获得的情感特征和语义特征基础上,将中心词信息和词汇到中心词的距离两个特征一起组成当前词的特征,然后,对双向LSTM神经网络进行情感词分类训练,从而得到动态情感词典.使用动态情感词典进行初级扩展也可以得到更大的静态情感词典.实验结果表明使用该动态情感词典进行微博情感分析可以有效地提高分类精度.
Text sentiment analysis is an important branch of Natural Language Processing,and it is also a hot topic in current research.The most basic and crucial part of emotion analysis is to build a high quality emotion dictionary. In order to overcome the shortcomings of the traditional affective lexicons,such as emotional tendenability,this paper constructs a dynamic affective dictionary based on the structure of three layers of neural network. The first layer extracts the features of emotional information through the improved CBOW neural network;the second layer uses a bidirectional LSTMneural network to extract the two fork semantic dependency path features by using the two forked semantic dependency structure model,and the third layer,the current word features includes the center word information,the distance of vocabulary to the center word,the emotional features and semantic features obtained in the first two layers,which is the input for the bidirectional LSTM. Then,the bidirectional LSTMneural network is trained to classify emotional words,and a dynamic emotion dictionary is obtained. Using dynamic emotional lexicon for primary expansion can also get a larger static emotion dictionary. The experimental results showthat using the dynamic sentiment dictionary for micro-blog sentiment analysis can effectively improve the classification accuracy.
作者
李永帅
王黎明
柴玉梅
刘箴
LI Yong-shuai;WANG Li-ming;CHAI Yu-mei;LIU Zhen(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;School of Information Science and Technology,Ningbo University,Ningbo 315211,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2019年第3期503-509,共7页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(U1636111)资助
关键词
动态情感词典
语义依存
情感信息
双向LSTM
CBOW
dynamic affective dictionary
semantic dependency
emotional information
bidirectional LSTM
CBOW
