基于迭代神经网络的文本情感分析
首发时间:2015-12-10
摘要:随着互联网的飞速发展,信息的传播和增长也越来越快。如何有效的获取民众对相关事件、商品、活动等评价信息并挖掘出一些其中有价值的内容,成为互联网技术的一项研究热点。因此,互联网舆情分析技术应运而生。文本情感分析以判断用户发表信息的情感属性为目标,是舆情分析中的一项关键技术,对了解民众舆论倾向起重要作用。传统的情感分析算法存在特征维度过高、语句中词语时序信息识别不全面等问题。 本文改进了一种基于朴素贝叶斯的支持向量机融合分类算法,利用N元模型优化文档特征向量,有效改善了传统方法中特征维度问题,并加入了短语时序信息。然后,本文实现了一种基于迭代神经网络的语言模型,可有效获得语句的完整时序信息,从而改善了情感分析的准确性
For information in English, please click here
Sentiment Analysis Of Text Base On Recurrent Neural Network
Abstract:With the rapid development of Internet, the transmission of information is becoming more and more quickly. People interact more frequently in the information via the Internet. How to effective access to relevant events, product, activity assessment information and dig out some valuable content, become a study focus in the Internet technology. Therefore, the Internet public opinion analysis technology arises at the historic moment. The text sentiment analysis to determine user information emotional attributes is a key technology of the public opinion analysis, which play an important role to understand public opinion tendency. Traditional sentiment analysis algorithm has some problems, such as high dimension feature, comprehensive words sequence information in a statement. This paper improves a support vector machine (SVM) based on naive bayes fusion classification algorithm. n-gram model was used to optimize the document feature vector, effectively improved the characteristic dimension problem in traditional method, and joined the phrase sequence information. What's more, This paper implements a language model based on recurrent neural network model, which can effectively obtain statements complete sequence information, thus improving the accuracy of the sentiment analysis.
Keywords: sentiment analysis neural network RNN
基金:
论文图表:
引用
No.4668618112016714****
同行评议
共计0人参与
勘误表
基于迭代神经网络的文本情感分析
评论
全部评论0/1000