摘要
Web评论研究技术中,其情感分析就是将评论的情感极性进行褒贬分类的过程。本文将非负矩阵分解(nonnegative matrix decomposition,NMF)和支持向量机(support vector machine,SVM)相结合,构造出一种基于NMF的支持向量机(NMF-SVM)分类算法。该算法利用NMF对初始的"词—文档"向量矩阵进行有效降维,提取潜在语义,最后利用支持向量机对重新构造的"词-文本"向量模型进行情感分类。实验结果证明,该分类算法的准确率优于比传统的SVM算法,具有一定应用价值。
The research technique of Web review, the sentiment analysis is regarded as a classification process for review's emotional polarity. A support vector machine(NMF-SVM) classification algorithm based on NMF has been put forward, for which combine NMF(nonnegative matrix decomposition) and SVM(support vector machine). The algorithm using NMF for initial word-document vector matrix to reduce the dimension effectively, and to extract the latent semantic, finally using support vector machine to emotion classification that word- document vector model has reconstructed. The experimental results show that the accuracy of the classification algorithm is superior to the traditional SVM algorithm.
出处
《电脑知识与技术》
2016年第6X期167-170,共4页
Computer Knowledge and Technology
关键词
Web评论
情感极性分类
非负矩阵分解
支持向量机
web comments
emotional polarity classification
nonnegative matrix decomposition
support vector machine
