计算机应用研究2016，Vol.33Issue(10)：2902-2905,2909,5.DOI:10.3969/j.issn.1001-3695.2016.10.005
基于word embedding和CNN的情感分类模型
Sentiment classification model based on word embedding and CNN
摘要
Abstract
This paper tried to propose a method to solve the problem of sentiment classification by integrating word embedding and convolutional neural network (CNN).First of all,the method accomplished a training process with skip-gram model to gen-erate word embedding of each word in the dataset.Then,it created a two-dimensional feature matrix which was the combination of word embedding of each word in a training sample as the input of CNN model.Each iteration process of training,entries of feature matrix would also update as part of model parameters.Secondly,this paper proposed a CNN structure which was mainly composed of three different sizes of convolution kernels so as to complete the automatic extraction process of a variety of local abstract features.Compared with traditional machine learning algorithms,the proposed word embedding and CNN based senti-ment classification model has successfully improved classification accuracy by 5 .04%.关键词
卷积神经网络/自然语言处理/深度学习/词嵌入/情感分类Key words
convolutional neural network/natural language processing(NLP)/deep learning/word embedding/sentiment classification分类
信息技术与安全科学引用本文复制引用
蔡慧苹,王丽丹,段书凯..基于word embedding和CNN的情感分类模型[J].计算机应用研究,2016,33(10):2902-2905,2909,5.基金项目
国家自然科学基金资助项目（61372139）；国家教育部“春晖计划”科研资助项目 （）
