基于句法分析的评价搭配抽取及其倾向性分析

提出一种新的基于句法分析的方法可以提取出评价搭配.通过对大量的商品评论进行句法分析,得到了常用的评论模式集合.依靠这些模式对评价搭配进行抽取.同时,构建了一种映射集合,可以映射出隐式评价对象.对于评价对象的倾向性判别,通过hownet构建了情感种子词典,采用SO-PMI方法计算出其极性,并计算出极性强度.实验结果表明,该方法对于评价搭配的抽取和极性分析具有较好的精确率和召回率.     

土壤肥力数量化分级的研究(Ⅰ)──土壤肥力的定义、分级原则和基本公式

从土壤的定义和本质特征进行分析，认为土壤肥力必须以生长植物的能力为核心，因而提出其定义为：在一般条件下，土壤使植物形成生长量的能力：这一定义不同于现行的三种定义，既包含有未知因素和协调程度，又有利于土壤肥力数量化分级制的建立，并可适用于肥力的动态观测和比较．土壤肥力分级的基本公式：直接分级Y＝KX全年产量t／hm2．（K＝1），间接分级Y＝a＋bx限制因子.     

HPLC-DAD法同时测定三种来源丹参饮片中水溶性和脂溶性成分

测定不同来源丹参饮片中水溶性和脂溶性成分含量，为丹参的质量评价和合理利用提供数据支持。发展了HPLC-DAD同时测定丹参中的6种水溶性和脂溶性成分的方法，利用该方法对来源于山东、四川和云南的丹参饮片样品进行含量测定，结合聚类分析进行不同来源丹参饮片的差异性对比。结果表明：该方法简单、快速，可以实现丹参中不同极性成分的同时提取、分析；不同来源的丹参饮片中水溶性和脂溶性成分含量均存在一定的差异，来源于四川产区的丹参饮片中各成分含量最高，来源于山东产区的丹参饮片酮类成分含量优于云南产区，采用活性成分含量结合聚类分析可以实现其来源区分；不同来源的丹参饮片中各活性成分含量存在一定的差异。研究结果为丹参饮片的质量评价和合理利用提供了方法和数据支持。     

游客微博主题情感分析方法比较研究

针对饮食、娱乐、购物、景观、交通和住宿6个旅游主题, 基于机器学习方法, 开展游客微博主题情感分析方法比较研究。以人工标注的53140条赴日游客微博为数据基础, 应用两种机器学习模型开展建模实验, 并分析不同特征对建模效果的影响。实验结果显示, 两种模型的建模效果良好, 适用于游客微博主题情感分析, 其中最大熵模型效果略优于支持向量机。研究还表明, 在词特征的基础上引入表情符号和主题词进行特征扩展, 可以提高模型的建模效果。     

A modularity analysis method for forum situation prediction

A forum is a social network that consists of posters and the following comments made by netizens. Generally speaking, forum topics are evolving over time dynamically. In this paper, based on time series analysis and matrix modularity analysis, a novel prediction method is proposed through investigating the correlating influence of three key measurements: relationship strength, pillars, and change frequency of a forum topic. The method demonstrates that there exist some macroscopic and potential laws for forum situation prediction. Extensive experiments over large many datasets show the efficiency and effectiveness of the algorithms.     

Computational intelligence approach for uncertainty quantification using evidence theory

As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification(UQ) analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method.     

教师业务综合考核的数学方法

在建立考核指标系统的基础上 ,用层次分析法给出各指标和权重 ,从而形成较客观、合理的考核系统 .     

Forecasting private consumption: survey‐based indicators vs. Google trends

In this study we introduce a new indicator for private consumption based on search query time series provided by Google Trends. The indicator is based on factors extracted from consumption‐related search categories of the Google Trends application Insights for Search. The forecasting performance of the new indicator is assessed relative to the two most common survey‐based indicators: the University of Michigan Consumer Sentiment Index and the Conference Board Consumer Confidence Index. The results show that in almost all conducted in‐sample and out‐of‐sample forecasting experiments the Google indicator outperforms the survey‐based indicators. This suggests that incorporating information from Google Trends may offer significant benefits to forecasters of private consumption. Copyright © 2011 John Wiley & Sons, Ltd.     

基于情绪的市场认知风险和市场认知收益

非理性投资者的认知风险和认知收益受投资者情绪的影响.基于投资者情绪的非理性认知风险度量模型,并利用理性和非理性投资者的相互作用,构建市场认知风险和认知收益度量模型,分别研究理性和非理性认知风险、认知收益对市场认知风险、认知收益的偏离问题.结果表明,非理性认知风险和认知收益的偏离程度与理性和非理性投资者的市场价值权重以及非理性投资者的情绪有关.