摘要
情感分类旨在发现用户对热点事件的观点态度,但由于现今互联网短文本格式随意,语言规范性不够,所以目前传统方法的情感分类效果并不理想。面向大数据互联网短文本信息,本文提出一种基于深度卷积神经网络(Convolutional Neural Networks,CNNs)模型的互联网短文本分类。首先选择词向量作为原始特征,然后通过卷积神经网络进一步提取特征,最后训练出基于深度卷积神经网络的互联网短文本情感分类模型。实验结果表明,该模型不仅可以有效处理互联网短文本中的情感分类这一任务,而且明显提高了情感分类的准确率,平均提高约5%。
Sentiment classification aims to find the users＇ views on hot issues,but now the format of the short texts on the Internet is not normative,the effect of traditional sentiment classification method is not ideal. Facing the information of the short texts on the Internet of big data,this paper puts forward a deep convolution neural network（CNNs） model of the short text on the Internet. First it uses the Skip-gram in the Word2vec training model as the feature vector,then further extracts feature vector into CNNs,finally trains the classification model of the depth convolution neural network. The experimental results show that,compared with classification methods of traditional machine learning,this method not only can effectively handle emotion classification of the short texts on the Internet,but also improves the accuracy of emotion classification significantly,the average increased by about 5%.
出处
《计算机与现代化》
2017年第4期73-77,共5页
Computer and Modernization
基金
国家自然科学基金资助项目(U1304611)
河南省科技攻关计划项目(132102310284)
河南省教育厅科学技术研究重点项目(14A520015)
郑州市科技攻关项目(131PPTGG416-4)
关键词
互联网短文本
情感分类
卷积神经网络
自然语言处理
深度学习
short texts on the Internet
sentiment classification
convolutional neural networks（CNNs）
natural language processing
deep learning
