摘要
针对单通道卷积神经网络(CNN)视角单一、不能充分学习到文本的特征信息的问题,提出双通道CNN(DCCNN)算法。首先,采用Word2Vec训练词向量,利用词向量获得句子的语义信息;其次,采用两个不同的通道进行卷积运算,一个通道为字向量,另一个通道为词向量,利用细粒度的字向量辅助词向量捕捉深层次的语义信息;最后,通过不同尺寸的卷积核,发现句子内部更高层次抽象的特征。实验结果表明,所提DCCNN算法能够准确识别文本情感极性,其正确率和F1值均达到95%以上,相比逻辑回归算法、支持向量机(SVM)算法以及CNN算法等都有显著提升。
The single channel Convolutional Neural Network（CNN） cannot fully study the feature information of text with a single perspective. In order to solve the problem, a new Dual-Channel CNN（DCCNN） algorithm was proposed. Firstly, the word vector was trained by Word2 Vec, and the semantic information of sentence was obtained by using word vector. Secondly,two different channels were used to carry out convolution operations, one channel was the character vector and the other was the word vector. The fine-grained character vector was used for assisting word vector to capture deep semantic information.Finally, the convolutional kernels of different sizes were used to find higher-level abstract features within the sentence. The experimental results show that, the proposed DCCNN algorithm can accurately identify the sentiment polarity of text, its accuracy and F1 value are above 95%, which are significantly improved compared with the algorithms of logistic regression,Support Vector Machine（SVM） and CNN.
作者
李平
戴月明
吴定会
LI ping , DAI Yueming, WU Dinghui(School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, Chin)
出处
《计算机应用》
CSCD
北大核心
2018年第6期1542-1546,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61572237)~~
关键词
卷积神经网络
文本情感分析
词向量
字向量
卷积核
Convolutional Neural Network （CNN）
sentiment analysis
word vector
character vector
convolutional kernel
