基于LSTM的商品评论情感分析

随着电子商务的发展,产生了大量的商品评论文本.针对商品评论的短文本特征,基于情感词典的情感分类方法需要大量依赖于情感数据库资源,而机器学习的方法又需要进行复杂的人工设计特征和提取特征过程.本文提出采用长短期记忆网络（Long Short-Term Memory）文本分类算法进行情感倾向分析,首先利用Word2vec和分词技术将评论短文本文本处理为计算机可理解的词向量传入LSTM网络并加入Dropout算法以防止过拟合得出最终的分类模型.实验表明：在基于深度学习的商品评论情感倾向分析中,利用LSTM网络的短时记忆独特特征对商品评论的情感分类取得了很好的效果,准确率达到99%以上.     

一种基于粒运算的文本情感分类方法研究

随着Internet的迅猛发展,人们对事件的立场、观点和看法的文本信息每天都会在网上出现,对于这些评论,仅靠人工进行跟踪和分析显然是行不通的,人们开始关注并研究评论文本的主观性情感倾向分析。文本情感分类中,分类器的设计是其中最重要的一个环节。文本评论往往是针对某一个特定领域的产品,评论语句一般都是短短几句,并且词汇量小特征词的交叉比较多,在这种情况下,与那些基于统计方法的分类器比较,基于规则的分类器更具优越性。提出了一种基于粒运算的方法,通过建立粒网络生成分类规则,从而得到评论文本的情感倾向分类。     

基于情感倾向特征的文本情感聚类

利用领域本体对产品评论文本中的特征及其评价词进行抽取,并将特征评价词的情感倾向与特征所在句子的情感倾向进行特征表示,得到文本特征矩阵,在此基础上,利用K-means算法实现了文本的情感聚类。为了验证该方法的有效性,在真实汽车评论文本数据上进行实验,结果表明,基于特征的情感倾向表示的权重相比布尔权重和LDA特征权重的聚类结果,在聚类的纯度和F值上有明显提高。     

基于神经网络的文本倾向性分析系统的研究

随着信息时代的到来,网络评论数量急剧增加,对于这些网络评论的倾向性分析是网络舆情研究中的重要课题。为了对网络评论的倾向性进行分析,提出了基于神经网络的网络舆情文本倾向性分析算法,使用神经网络训练的方法构建了较好的情感词倾向值词典,对网络评论的倾向性预测达到了较高的准确率。对算法进行了细致的描述,同时提出了基于神经网络的网络舆情文本倾向性分析系统框架。最后通过实验进行测试,证明了该系统能够达到较好的准确率。     

微博信息监测应用与算法研究

分析了微博文本的特点,设计了微博信息监测应用的功能。采用VSM模型表示微博文本,并基于SVM分类算法对微博评论进行情感分类;结合评论者的身份信息对其权威性进行计算。最后综合每位评论者的情感倾向及其权威性,得到大众对该微博话题的情感倾向。     

面向中文网络评论情感分类的集成学习框架

该文针对中文网络评论情感分类任务,提出了一种集成学习框架。首先针对中文网络评论复杂多样的特点,采用词性组合模式、频繁词序列模式和保序子矩阵模式作为输入特征。然后采用基于信息增益的随机子空间算法解决文本特征繁多的问题,同时提高基分类器的分类性能。最后基于产品属性构造基分类器算法综合评论文本中每个属性的情感信息,进而判别评论的句子级情感倾向。实验结果表明了该框架在中文网络评论情感分类任务上的有效性,特别是在Logistic Regression分类算法上准确率达到90.3%。     

基于卷积自注意力联合模型的评论情感分析

随着移动互联网的兴起,人们可以在网络上自由发表各种感想和评论,文本情感分析已经成为了自然语言处理中的一个重要研究方向.应用深度学习算法进行评论文本情感分析已经成为研究热点.论文应用了一种基于多通道卷积神经网络和双向长短时记忆神经网络融合的深度学习算法,获取文本的多粒度局部语义特征与全局语义特征,同时引入自注意力机制,提...     

改进的CBOW情感信息获取研究

大数据时代,文本的情感倾向对于文本潜在价值挖掘具有重要意义,然而人工方法很难有效挖掘网络上评论文本的潜在价值,随着计算机技术的快速发展,这一问题得到了有效解决。在文本情感分析中,获取词语的情感信息对于情感分析至关重要,词向量方法一般仅对词语的语法语义进行建模,但是忽略了词语的情感信息,无法更好地进行情感分析。通过TF-IDF算法模型获得赋权矩阵,构建停用词表,同时根据赋权矩阵生成Huffman树作为改进的CBOW算法的输入,引入情感词典生成情感标签辅助词向量生成,使词向量具有情感信息。实验结果表明,提出的方法对评论文本中获得的词向量能够较好地表达情感信息,情感分类结果优于传统模型。因此,该模型在评论文本情感分析中可以有效提升文本情感分类效果。     

基于语义约束LDA的商品特征和情感词提取

随着网络购物的发展,Web上产生了大量的商品评论文本数据,其中蕴含着丰富的评价知识。如何从这些海量评论文本中有效提取商品特征和情感词,进而获取特征级别的情感倾向,是进行商品评论细粒度情感分析的关键。本文根据中文商品评论文本的特点,从句法分析、词义理解和语境相关等多角度获取词语间的语义关系,然后将其作为约束知识嵌入到主题模型,提出语义关系约束的主题模型SRC-LDA（semantic relation constrained LDA）,用来实现语义指导下LDA的细粒度主题词提取。由于SRC-LDA改善了标准LDA对于主题词的语义理解和识别能力,从而提高了相同主题下主题词分配的关联度和不同主题下主题词分配的区分度,可以更多地发现细粒度特征词、情感词及其之间的语义关联性。通过实验表明,SRC-LDA对于细粒度特征和情感词的发现和提取具有较好的效果。     

大数据时代中文文本褒贬倾向性分类研究

在当前的大数据时代,互联网上的博客、论坛产生了海量的主观性评论信息,这些评论信息表达了人们的各种情感色彩和情感倾向性。如果仅仅用人工的方法来对网络上海量的评论信息进行分类和处理实在是太难了,那么,如何高效地挖掘出网络上大量的具有褒贬倾向性观点的信息就成为目前亟待解决的问题,中文文本褒贬倾向性分类技术研究正是解决这一问题的一个方法。文章介绍了常用的文本特征选择算法,分析了文档频率和互信息算法的不足,通过对两个算法的对比和研究,结合文本特征与文本类型的相关度和文本褒贬特征的出现概率,提出了改进的文本特征选择算法（MIDF）。实验结果表明,MIDF算法对文本褒贬倾向性分类是有效的。     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.