基于因子分析和Elman网络的舆情关键词热度预测

从宏观角度研究基于关键词的网络舆情热度有助于相关机构把握目标群体的整体舆情动态,从而实现精准施策,提升舆论引导水平.本文以新浪微博数据为例,采用因子分析方法(Factor Analysis,FA),挖掘舆情热度内在影响因素,并通过改进Elman网络结构,利用遗传算法(Genetic Algorithm,GA)优化初始参...     

文本立场检测综述

文本立场检测是文本意见挖掘领域的基础性研究,旨在分析文本中对特定目标所表现的立场倾向.随着互联网的飞速发展,用户对于公共事件、消费产品等的讨论文本呈指数级增长,文本立场检测研究对产品营销、舆情决策等具有重要意义.从目标类型、文本粒度以及研究方法3个角度对文本立场检测研究工作展开综述.首先,从目标类型角度,围绕单目标、多目标以及跨目标立场检测3个方面梳理了文本立场检测的不同研究任务;从文本粒度角度,对比了句子级、篇章级以及辩论文本立场检测的不同研究场景和方法;从研究方法角度,介绍了基于传统机器学习、主题模型、深度学习以及“2阶段”的方法,并指出各种方法的可取与不足之处.接着,对文本立场检测评测任务以及公开数据资源进行了归纳.最后,立足当前研究形势,总结了文本立场检测研究的应用领域,展望了未来发展趋势以及面临的挑战.     

基于Moran过程的竞争性舆情信息的网络传播博弈模型

新的媒体环境下,在享受不同形式在线社交网络提供便捷信息互动渠道的同时,需要加强对信息传播的管理与引导,防止其发展成为负面舆情信息肆意滋生的温床。结合传统演化博弈论与复杂网络理论,提出了竞争性舆情信息的网络传播博弈模型,并理论分析了正面舆情信息传播占优的收益条件及群体规模;随后,结合实际案例,通过仿真实验对比分析三种博弈环境下网络舆情的演化特征。三种博弈环境下,正面舆情信息传播策略占优条件均与收益有关,弱选择情境对收益参数的要求最为严格;强、弱选择情景下策略占优的条件还与群体规模有关,且强选择情景对群体规模的要求更为严格。     

PUBLIC OPINION,TRADITION, AND INFORMATION HALF-LIFE†

The information needs of a complex society are not met by an information-flow technology invented and developed in traditional societies. This article provides a theoretical framework derived from modern systems theory by which to understand the technology of public opinion policy formation in guidance operations of a complex society. Qualitative models of information flow systems are presented together with a discussion of the kind of society each information flow technology produces. Three are considered: tradition-based information-flow societies, managed information-flow societies (Utopian and elitist), and cybernetic issue-based information-flow societies.     

AN OPINION FEATURE EXTRACTION APPROACH BASED ON A MULTIDIMENSIONAL SENTENCE ANALYSIS MODEL

With Web 2.0 applications being widely used, social networking services, including web blogs, forums, and other online communities, have become informative tools that help individuals to easily gauge the pulse of the electronic consuming market. As a substitute for traditional public media, the related site provides unique mechanisms to instantly reveal the degree of public product acceptance by either statistically aggregating the rating results or archiving opinions shared by experienced customers. However, the growth of user-generated information and its scattered unstructured contents is overwhelming to users, thereby triggering the demand for a more efficient system that can offer concise information. Most existing efforts dedicated to these issues may neglect vital aspects of the sentence-level context. This article aims to explore the critical features hidden in the sentential structure of opinion articles and expects that the detected patterns may contribute to the enhancement of related applications. Accordingly, a multidimensional sentence modeling algorithm (MSMA) is designed to evaluate various sentential characteristics and adopt a genetic algorithm to optimize the weighting scheme while determining feature importance. The study also makes use of the public knowledge resource Wikipedia as a global reference to fine-tune the feature set's effectiveness and enhance the overall performance of this framework. The results of experiments on an electronic product data set demonstrate that the proposed method is promising and provides significant improvement over previous studies.     

Exploiting opinion distribution for topic recommendation in Twitter

The popular social networking service Twitter provides rapidly updated information and online trends, which enriches and benefits people's daily life. At the same time, how to find out the really interesting and relevant topics from the massive streams of tweets, to provide precise topic recommendation for users, becomes a challenging problem in the real world. Previous collaborative filtering methods give solutions to traditional recommendation tasks considering users' positive reviews to help recommend items. However, to the problem ‘what is interesting to whom’ in Twitter, positive opinions toward a topic do not imply that the user will be interested in it with high probability, for the user probably prefers to know those controversial topics or hot events with a large number of negative posts. In this paper, we exploit the characteristics of topical opinion distribution for improving the performance of recommendation. The experimental results on a real‐world Twitter dataset show that the proposed opinion‐distribution‐aware topic recommendation (ODA‐TR) approach outperforms the state‐of‐the‐art collaborative recommendation methods. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

工程招标代理机构信用体系建设探讨——以福建省和黑龙江省为例

信用评价是建筑市场信用体系建设的重要方面。但目前在招标代理机构的信用评价方面,仅有福建省与黑龙江省出台了用于评定招标代理机构信用等级的《暂行办法》。首先通过对比指出两省《暂行办法》各自的特点,并对不足之处提出修改建议。然后,鉴于客户意见在信用评价中的重要性,针对福建省的"招标代理评价意见表"提出两点修改建议,并建立了改进的意见表。通过这些分析,希望能在其他地区制定招标代理机构信用评价制度时起到一定参考作用,从而推进招标代理机构及整个建筑市场的信用体系建设。     

两种线程池的实现和性能评价

线程池被广泛地应用在中间件如Web应用服务器、事务监控器等的实现中.实现线程池的方法主要有半同步/半异步(Half-Sync/Half-Asyn)模式和领导者/跟随者(Leader/Follower)模式.实现了这两种线程池,从理论上分析了它们的性能,并进行了性能实验.实验数据表明Leader/Follower模式具有较高的性能优势.     

食品安全网络舆情监测应用进展与社会共治模式研究

食品安全网络舆情监测系统在国内外被应用于食品安全风险预警与舆情补救,开发决策参考、监督抽检、专项整治、协查处置等舆情信息应用场景化服务,实现高并发情形下对热点食品安全舆情事件的自动跟踪和分析。为探明该系统进一步的发展方向,本文研究比较了欧美食品安全舆情监测系统的应用状况,分析了不同社会主体应用食品安全舆情监测系统的领域,结合我国实际发展制定相应措施,得出食品安全舆情监测系统与社会共治主体密切联系的结论。从而提出政府、食品企业和消费者三个社会主体在食品安全网络舆情监测系统的推广和应用上的指导方案。同时,食品安全舆情监测有待于借助大数据技术进一步发展,从而开拓出更加丰富的使用功能,以保障人民的食品安全。     

基于强化学习的合作频谱分配算法

为了解决认知无线电网络中的频谱分配问题,提出了一种基于用户体验质量的合作强化学习频谱分配算法,将认知网络中的次用户模拟为强化学习中的智能体,并在次用户间引入合作机制,新加入用户可以吸收借鉴其他用户的强化学习经验,能够以更快的速度获得最佳的频谱分配方案;并且在频谱分配过程中引入了主用户和次用户之间的价格博弈因素,允许主用...