共查询到19条相似文献，搜索用时 88 毫秒
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基于种子文档LDA话题的演化研究 总被引：1，自引：0，他引：1
提出一种基于种子文档的LDA话题演化方法。首先选取种子文档,利用种子文档指导后一时间段文档的建模,然后根据种子文档的语义分布信息对连续时间上的LDA话题进行关联,保证话题的同一性。实验结果证明,在NIPS论文语料集和全国两会新闻报道集中,该方法可以推导特定话题的演化结果,避免关联话题之间存在的演化结果。 相似文献
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网络社区有影响力话题度量识别方法研究 总被引：2，自引：0，他引：2
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社交平台是网民传达观点和情感的重要途径,分析社交平台话题分布及演化过程能够揭示舆情热点及传播发展过程,对引导公众舆论具有重要的参考作用。本研究利用网络社团演化的方法检测社交平台话题并分析其演化过程。首先,对用户发布的文本内容进行时间切片,构建时序共词网络并提取各时间切片的主干网络,利用Leiden算法检测社团来表示话题。其次,提出基于社团正向和反向转移概率及社团规模的话题演化事件检测方法,识别话题演化中的持续、增长、收缩、合并、分裂、新生以及消亡等事件。以新浪微博平台新冠肺炎疫情相关微博为例,在话题检测中发现,主干网络相较于原始网络能够检测到更多话题,话题内容区分粒度更细。在话题演化分析中,发现了公众情绪由消极转积极、防控和医疗工作专业化、国际疫情蔓延态势及疫情对经济的影响逐步扩大等演化路径。 相似文献
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基于Single-Pass算法思想,研究网络话题的在线聚类方法,以期及时捕捉网络信息的动态变化在分析该方法聚类流程的基础上,重点研究网络动态信息流的文本特征抽取和权重计算方法,以及话题类表示和更新等关键问题,设计实验对比分析不同的标题中特征加权系数、特征权重计算和标准化方法以及话题类向量维度对话题聚类质量和时间效率的影响。 相似文献
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传统学科主题研究主要基于学术文本题录数据,研究对象单一。本文以SIGIR(Special Interest Group on Information Retrieval)邮件列表为切入点分别构建SIGIR邮件列表数据集和同期会议论文数据集,并在两个数据集的基础上对信息检索的主题结构和主题演化进行对比分析。研究发现,信息检索领域存在研究内容不断深入、研究方法不断增多和核心主题逐渐分裂的规律;同时还发现,SIGIR邮件列表研究主题较会议论文而言,在时序上存在一定的"领先性",通过该研究旨在揭示SIGIR邮件列表在信息检索领域的学术价值。 相似文献
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传统的基于BTM的话题发现方法未考虑大数据条件下,海量短文本中热点话题发现存在的时效性限制问题。基于Spark计算框架、BTM模型和K-means算法,提出了并行旅游舆情热点话题发现算法,通过对旅游评论、微博短文本集的词对生成、文档-话题分布矩阵、文档相似度计算及聚类过程进行基于Spark框架的并行化,缩短了热点话题的发现时间,提高了实时性。实验结果显示本算法加速比和扩展性相比单一BTM模型能进一步提升,适用于旅游舆情热点话题发现的应用需求。 相似文献
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[目的/意义]探索热点事件评论网络中话题社群及网民的情感波动,掌握舆情事件发展过程,对于整体把握热点事件的发展方向,做好新时期网络舆论的引导工作具有重大意义。[方法/过程]以复杂网络理论为基础,基于评论词语间的共现关系构建基于事件发展的子事件网络,通过社群发现算法来识别子事件评论网络中的话题社群,将情感词依据情感词典赋予情感分类属性,基于事件的演化过程动态地跟踪网民意见以及情感波动。[结果/结论]研究结果表明,评论网络群落发现以及变异系数方法可以有效地衡量网民话题讨论的规模与集中程度;评论网络中赋予情感词节点情感分类属性方法可以体现事件演化过程中网民的情感变化;舆论衍生话题对事件的舆情发展有持续性影响;网民话题讨论内容对于事件演化具有一定程度上的前瞻性。 相似文献
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《Journal of Informetrics》2014,8(1):98-110
Dynamic development is an intrinsic characteristic of research topics. To study this, this paper proposes two sets of topic attributes to examine topic dynamic characteristics: topic continuity and topic popularity. Topic continuity comprises six attributes: steady, concentrating, diluting, sporadic, transforming, and emerging topics; topic popularity comprises three attributes: rising, declining, and fluctuating topics. These attributes are applied to a data set on library and information science publications during the past 11 years (2001–2011). Results show that topics on “web information retrieval”, “citation and bibliometrics”, “system and technology”, and “health science” have the highest average popularity; topics on “h-index”, “online communities”, “data preservation”, “social media”, and “web analysis” are increasingly becoming popular in library and information science. 相似文献
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对《时代》周刊和《三联生活周刊》2010年所有封面故事的主题类别进行分类统计,并从选题类型、选题分布、特刊选题和封面标题四个方面分析两刊的选题情况,发现两刊除了同样关注社会类选题外,在选题侧重点、选题视野、对读者的引导和塑造以及标题的制定等方面都存在较大差别。 相似文献
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微博网络中信息的"裂变式"传播模式对已有舆情传播模型提出挑战。为有效地揭示出微博用户关注关系所形成的复杂网络中舆情传播演化的机理,以有向无标度网络为载体提出舆情传播的SIRS模型,该模型综合考虑微博的传播特性以及舆情话题的衍生性等因素,并对模型进行仿真分析,其结果可以验证模型的有效性。 相似文献
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基于被引次数的引文分析无法直接揭示论文的研究内容,利用关键词或从标题、摘要和全文中抽取的主题词很难客观反映论文的被引原因。本文以碳纳米管纤维研究领域的高被引论文为研究对象进行引文内容抽取和主题识别,经人工判读验证：基于引文内容分析的高被引论文识别的核心主题能够较好地揭示高被引论文的被引原因（引用动机）,而且与论文的研究内容相符合;与基于全文、基于标题和摘要的主题识别相比,在引文内容分析基础上识别的主题具有更好的主题代表性,能够有效揭示被引文献的研究内容,是对原文相关信息的重要补充。本文的实验表明基于引文内容分析的高被引论文主题识别是可行而且有效的。图4。表4。参考文献31。 相似文献
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This study examined adolescents' and young adults' use of topic avoidance with their mothers, fathers, stepmothers, and stepfathers. The types of topics avoided differed according to the type of parent-child relationship. Specifically, adolescents and young adults engaged in the most topic avoidance with their stepparents (regardless of whether the stepparent was a stepmother or stepfather), followed by their fathers, and then their mothers. Quantitative measures indicated that sex was the most frequently avoided topic across all relationship types. Open-ended responses revealed additional commonly avoided topics, including talking about the other parent/family, deep conversations, and money (e.g., child support payments). The most frequently reported reasons for this avoidance were self protection, relationship protection, and conflict. This research suggests that children in stepfamilies face unique decisions about topic avoidance. Communication Boundary Management Theory (Petronio, 1991) was used to explain how adolescents and young adults might engage in topic avoidance to regulate their personal boundaries, constructing relatively impermeable boundaries with some adults while maintaining looser boundaries with others. Finally, numerous practical suggestions are offered for understanding the balance between openness and closedness in stepfamilies and for promoting healthy stepfamily functioning. 相似文献
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This study examined adolescents' and young adults' use of topic avoidance with their mothers, fathers, stepmothers, and stepfathers. The types of topics avoided differed according to the type of parent-child relationship. Specifically, adolescents and young adults engaged in the most topic avoidance with their stepparents (regardless of whether the stepparent was a stepmother or stepfather), followed by their fathers, and then their mothers. Quantitative measures indicated that sex was the most frequently avoided topic across all relationship types. Open-ended responses revealed additional commonly avoided topics, including talking about the other parent/family, deep conversations, and money (e.g., child support payments). The most frequently reported reasons for this avoidance were self protection, relationship protection, and conflict. This research suggests that children in stepfamilies face unique decisions about topic avoidance. Communication Boundary Management Theory (Petronio, 1991) was used to explain how adolescents and young adults might engage in topic avoidance to regulate their personal boundaries, constructing relatively impermeable boundaries with some adults while maintaining looser boundaries with others. Finally, numerous practical suggestions are offered for understanding the balance between openness and closedness in stepfamilies and for promoting healthy stepfamily functioning. 相似文献
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Emerging research topic detection can benefit the research foundations and policy-makers. With the long-term and recent interest in detecting emerging research topics, various approaches are proposed in the literature. Though, there is still a lack of well-established linkages between the clear conceptual definition of emerging research topics and the proposed indicators for operationalization. This work follows the definition by Wang (2018), and several machine learning models are together used to detect and foresight the emerging research topics. Finally, experimental results on gene editing dataset discover three emerging research topics, which make clear that it is feasible to identify emerging research topics with our framework. 相似文献