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1.
命名实体的网络话题K-means动态检测方法   总被引:1,自引:0,他引:1  
针对传统的网络话题检测方法在文本特征表示方面的不足及K-means聚类算法面临的问题,提出了一种基于命名实体的网络话题K-means动态检测方法.该方法对传统话题检测的特征表示方法进行了改进,用命名实体和文本特征词相结合表示文本特征,用命名实体对文本表示的贡献大小表示命名实体的权重;另外,利用自适应技术对K-means聚类算法中的K值进行自收敛,对K-means聚类算法进行了优化,利用K值的动态选取来实现网络话题的动态检测.实验结果表明,该方法较好地区分了相似话题,有效提高了话题检测的性能.  相似文献   

2.
通过基于随机游走的网络表示学习算法得到节点的低维嵌入向量,进而将其应用于推荐系统是推荐领域很流行的研究方向.针对当前基于随机游走的网络表示学习算法仅着重考虑了网络结构特性而忽略文本信息的问题,提出一种关联文本信息的网络表示学习推荐算法.首先在随机游走阶段,考虑到了节点文本间的相似度,联合结构和文本信息对下一游走节点进行筛选;然后在网络表示学习部分融合文本信息,引入注意力矩阵,对文本信息矩阵中的向量进行加权表示;最后将生成的节点向量应用于推荐系统.在实验部分,将所提算法与常见的3种算法在两个数据集上进行对比分析,并对所提算法进行了参数敏感性分析.实验结果表明所提算法在AUC评价指标上的性能优于另外3种算法,可见该算法在个性化推荐中的有效性.  相似文献   

3.
通过分析已有的基于统计和基于语义分析的文本相似性度量方法的不足,提出了一种新的基于语言网络和词项语义信息的文本相似度计算方法。对文本建立语言网络,计算网络节点综合特征值,选取TOP比例特征词表征文本,有效降低文本表示维度。计算TOP比例特征词间的相似度,以及这些词的综合特征值所占百分比以计算文本之间的相似度。利用提出的相似度计算方法在数据集上进行聚类实验,实验结果表明,提出的文本相似度计算方法,在F-度量值标准上优于传统的TF-IDF方法以及另一种基于词项语义信息的相似度量方法。  相似文献   

4.
在当今处于信息数量爆炸式增长的互联网时代,如何分析海量文本中的信息并从而提取出所蕴含的有利用价值的部分,是一个值得关注的问题。然而论坛语料作为网络语料,其结构和内容较一般语料相比更为复杂,文本也更加短小。该文提出的方法利用LDA模型对语料集进行建模,将话题从中抽取出来,根据生成的话题空间找到相应的话题支持文档,计算文档支持率作为话题强度;将话题强度反映在时间轴上,得到话题的强度趋势;通过在不同时间段上对语料重新建模,并结合全局话题,得到话题的内容演化路径。实验结果说明,上述方法是合理和有效的。  相似文献   

5.
话题演进分析主要是挖掘话题内容随着时间流的演进情况。话题的内容可用关键词来表示。利用word2vec对75万篇新闻和微博文本进行训练,得到词向量模型。将文本流处理后输入模型,获得时间序列下所有词汇的词向量,利用K-means对词向量进行聚类,从而实现话题关键词的抽取。实验对比了基于PLSA和LDA主题模型下的话题抽取效果,发现本文的话题分析效果优于主题模型的方法。同时,采集足够大量、内容足够丰富的语料,可训练得到泛化能力比较强的模型,有利于实时话题演进分析研究工作。  相似文献   

6.
常规的文本匹配模型大致分为基于表示的文本匹配模型和基于交互的文本匹配模型.由于基于表示的文本匹配模型容易失去语义焦点,而基于交互的文本匹配模型会忽视全局信息,文中提出了结合多粒度信息的文本匹配融合模型.该模型通过交互注意力和表示注意力将两种文本匹配模型进行了融合,然后利用卷积神经网络提取了文本中存在的多个不同级别的粒度信息,使得模型既能抓住局部的重要信息又能获取全局的语义信息.在3组不同的文本匹配任务上的实验结果表明,所提出的模型在NDCG@5评价指标上分别优于其他最优模型5.3%,0.4%,1.5%.通过提取文本中的多个粒度信息并结合交互注意力和表示注意力,提出的模型能够有效地关注不同级别的文本信息,解决了传统模型在文本匹配过程中易失去语义焦点和忽视全局信息的问题.  相似文献   

7.
信息的暴涨给文本处理带来了更多的挑战。话题检测能够把大量的信息以话题为单位有效地组织起来,然而最终用户有可能并不需要涉及某一话题的所有文本,而是仅仅关心该话题的具体内容。在我们根据相关文本智能表达话题内容推送给用户之前,自动从相关文本中挑选符合用户需求的文本是一个非常有意义的工作。本文致力于相同话题文本之间的内容比较,目的是有效地选出满足需求的文本。我们通过对话题进行重新定义,并根据此定义设定了话题和文本的表示方法,给出了基于该表示方法的话题和文本之间的内容比较计算方法。最后,通过实验说明了这一系列方法的有效性。
  相似文献   

8.
针对微博的实时性、稀疏性和海量性特点,提出基于实时词共现网络的话题发现模型。首先,从原始语料中筛选出主题词集合,再利用时间参数计算共现主题词的关系权重以实现词共现网络的构建,通过该网络推算出与话题关联性强的潜在特征词以解决微博特征词的稀疏性;其次,采用改进Single-Pass算法实现话题增量聚类;最后,对每个话题的主题词按热度计算进行排序,获得最具代表性的话题主题词。实验结果表明,该模型与经典Single-Pass聚类算法相比,话题发现准确率约提高6%,综合指标提高8%。实验结果证明所提模型的有效性和准确性。  相似文献   

9.
近年来,随着信息全球化的影响,社交网络文本上的多语言混合现象越来越普遍。许多中文文本中混杂着其他语言的情况已很常见。绝大多数现有的自然语言处理算法都是基于单一语言的,并不能很好地处理多语言混合的文本,因此在进行其他自然语言处理任务之前对文本进行预处理显得尤为重要。面对网络文本语义空间双语对齐语料的匮乏,提出一种基于话题翻译模型的方法,利用不同语义空间的语料计算网络文本语义空间的双语对齐概率,再结合神经网络语言模型将网络混合文本中的英文翻译成对应中文。实验在人工标注的测试语料上进行,实验结果表明,通过不同的对比试验证明文中的方法是有效的,能提升翻译正确率。  相似文献   

10.
话题演化挖掘研究可以准确完整地获取新闻话题动态演化各个阶段的话题内容,帮助用户理解新闻话题的来龙去脉以及话题内容之间的相关性和差异性,因此在网络新闻检索、网络舆情监控、互联网突发事件检测与应急管理等方面具有十分重要的作用和应用前景.现有工作由于缺乏对话题特征随时间发展而动态演变的深入分析,仅仅采用均值泛化的思想去增量扩充演化中的话题特征,引入大量话题无关信息,影响了话题关联的准确率,从而导致最终话题演化挖掘结果的偏斜.因此,针对以上问题,文中通过引入话题特征演变特性,提出一种针对话题演化的特征计算模型,在此基础上利用已有话题相关文档和最新文档进行话题信息动态增量扩充,通过对话题特征进行正向融合以及逆向过滤完成对特征信息的抗噪处理,提高话题关联的正确率,有效地解决了话题演化的偏斜问题.  相似文献   

11.
Reflecting on a feasibility study into archiving social media, this article traces how “events” are defined in various domains and contexts, and employs case studies to analyze key relationships between hashtags and events to provide a critical analysis of how archival events can be constructed out of social events. It provides an overview of the archival and curatorial considerations involved in defining and preserving a social media event, and outlines the technologies developed for the process of collecting, annotating, and preserving social media events. Overall, the article endeavors to reveal how pragmatic considerations, computational approaches and curatorial perspectives shape digital archives and historical narratives.  相似文献   

12.
BBS中信息传播模式的特征分析   总被引:2,自引:1,他引:1       下载免费PDF全文
通过比较传染病传播机制与信息传播机制,提出BBS中的信息传播机制模型。通过对BBS中帖子数量变化规律的建模,分析了BBS中信息传播模式的特征,并使用实际数据说明BBS中的信息传播模式。实验表明:BBS可以吸引大批的用户参与,但用户只对部分话题感兴趣并参与讨论;绝大多数话题(占94.9%)帖子数的增长率先增加再减小直至为0,而少量话题(占5.1%)帖子数的增长率直接减小至0。这些结论有助于认识BBS的信息传播机制,对控制和管理BBS的信息传播有启发意义。  相似文献   

13.
The use of social media to share information, enhance learning, and connect with an online community has grown rapidly over the past 10 years. As social media becomes a more common tool in both formal and informal education, it is imperative to understand how it is used by individuals with disabilities. Through a systematic study of the literature, 215 articles on social media used by individuals with disabilities were selected and 29 selected for in-depth thematic analysis. Six major themes were identified: community, cyberbullying, self-esteem, self-determination, access to technology, and accessibility. To confirm these six categories, we expanded our search, yielding an additional 30 articles, for a total 59 articles reviewed in-depth. Interactions between individuals with disabilities within online communities often had the goal of acquiring knowledge or learning new information. A communities of practice theoretical framework is used to discuss interactions among the elements of social media design, learning, and the building of community by individuals with disabilities.  相似文献   

14.
网络实名制的提出,是为了解决网络匿名性所带来的问题,却又面临实名信息泄露的诟病。造成信息泄露的根源在于实名认证依赖于实名信息。基于社会认证的网络身份模型,依赖社会关系进行身份认证,其利用OSN节点的社会关系构建网络身份,在发挥网络监管作用的同时,避免实名信息的泄漏。模型首先在OSN中依据一定策略选择根节点;然后,采用担保方式进行社会认证;最后,在不依赖实名信息的基础上,构建节点的唯一网络身份SANI。SNAI身份含节点的社会认证信息,具有身份认证和行为溯源的功能。  相似文献   

15.
This paper explores the affordances of social technologies for supporting the construction of a shareable artefact by a group of learners. A qualitative study that captures the use of five different types of social technologies (Facebook, blogs, wikis, Google Documents and Dropbox) in three different classroom settings sheds light on the potentials and challenges of these tools for supporting material exploration, artefact construction and evaluation. Qualitative content analysis of instructors’ field notes, students’ and instructors’ reflections, interviews and focus groups sheds light on the potential of social technologies to transform the activity of learning across a new culture of computational tools. The affordances of social technologies are discussed as well as design principles that need to be followed in these new arenas.  相似文献   

16.
This study explores the relationship between perceived bridging social capital and specific Facebook‐enabled communication behaviors using survey data from a sample of U.S. adults (N=614). We explore the role of a specific set of Facebook behaviors that support relationship maintenance and assess the extent to which demographic variables, time on site, total and “actual” Facebook Friends, and this new measure (Facebook Relationship Maintenance Behaviors) predict bridging social capital. Drawing upon scholarship on social capital and relationship maintenance, we discuss the role of social grooming and attention‐signaling activities in shaping perceived access to resources in one's network as measured by bridging social capital.  相似文献   

17.
在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。  相似文献   

18.
Posting behaviour on social networking sites (SNS) has become a method enabling unsatisfied users to vent emotions. Based on social cognition theory (SCT), personal outcome expectations and self-efficacy affect posting behaviour for venting emotions on SNS. However, perceived social support (PSS) may alter the relationships within the SCT model. Thus, this study aimed to explore the moderating effect of PSS on the relationships between variables in the SCT model for venting emotions on SNS. In total, 310 unsatisfied customers in Taiwan were investigated, and structural equation modelling was performed to test the hypotheses. The results indicated that personal outcome expectations and self-efficacy were positively associated with posting behaviour which, in turn, increased venting emotions on SNS. Moreover, PSS moderated the relationships between variables in the SCT model.  相似文献   

19.
Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

20.
Nowadays,more and more users share real-time news and information in micro-blogging communities such as Twitter,Tumblr or Plurk.In these sites,information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows,named followees.With the increasing number of registered users in this kind of sites,finding relevant and reliable sources of information becomes essential.The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation.To address this problem,we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources.Experimental evaluation conducted with a group of users is reported,demonstrating the potential of the approach.  相似文献   

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