社交媒体文本数据可视化建模:一种及时的主题评分技术

Zhenhua Sui
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引用次数: 8

摘要

由于来自Twitter等互联网来源的大尺寸文本数据的快速增长,社交媒体平台已成为更受欢迎的信息提取来源。将提取的文本信息通过一系列的数据转换进一步转换为数字,再通过文本分析模型进行分析,解决决策问题。在文本分析模型中,一种特别常见和流行的是基于潜狄利克雷分配(Latent Dirichlet Allocation, LDA)的文本分析模型,它是一种主题模型方法,主题是与拟合的多元统计分布相关的文档中单词的聚类。然而,这些模型通常不能很好地估计主题比例。因此,本文提出了一种社交媒体文本数据可视化的实时主题评分技术,该技术基于主题模型的积分系统来支持文本信令。该重要性评分系统旨在通过使用主题比例输出和分配重要性点来呈现文本主题趋势来缓解主题模型的弱点。然后,该技术生成可视化工具来显示研究期间的主题趋势,然后进一步促进决策问题。最后,本文研究了两个来自Twitter文本来源的现实案例,并说明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Media Text Data Visualization Modeling: A Timely Topic Score Technique
Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.
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