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TIMELINE VISUALIZATION OF KEYWORDS

机译:关键字的时间轴可视化

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摘要

Visualizations of communications between actors are typically presented as actor interactions or as plots of the dates and times when the communications occurred. These visualizations are valuable to forensic analysts; however, they do not provide an understanding of the general flow of the discussed topics, which are identified by keywords or keyphrases. The ability to view the content of a corpus as a timeline of discussion topics can provide clues to when certain topics became more prevalent in the discussion, when topics disappeared from the discussion and which topics are outliers in the corpus. This, in turn, may help discover related topics and times that can be used as clues in further analyses. The goal is to provide a forensic analyst with assistance in systematically reviewing data, eliminating the need to manually examine large amounts of communications. This chapter focuses on the timeline-based visualization of keywords in a text corpus. The proposed technique employs automated keyword extraction and clustering to produce a visual summary of topics recorded from the content of an email corpus. Topics are regarded as keywords and are placed on a timeline for visual inspection. Links are placed between topics as the timeline progresses. Placing topics on a timeline makes it easier to discover patterns of communication about specific topics instead of merely focusing on general discussion patterns. The technique complements existing visualization techniques by enabling a forensic analyst to concentrate on the most interesting portions of a corpus.
机译:演员之间的通信的可视化通常作为演员交互或作为通信发生时的日期的曲线呈现。这些可视化对法医分析师有价值;但是,它们不提供对讨论的主题的一般流程的理解,这些主题由关键字或密钥段识别。将语料库内容视为讨论主题的时间线的能力可以提供线索,当某些主题在讨论中变得更加普遍时,当主题从讨论中消失时,语料库中的异常符号都是其中的主题。反过来,这可能有助于发现可以在进一步分析中用作线索的相关主题和时间。目标是在系统地审查数据方面提供一项法医分析师,无需手动检查大量通信。本章侧重于文本语料库中的基于时间轴的可视化。所提出的技术采用自动关键字提取和聚类来生成从电子邮件语料库内容记录的主题的视觉摘要。主题被视为关键字,并放置在视觉检查的时间表上。随着时间线的进行,链接位于主题之间。在时间线上放置主题使得可以更容易地发现有关特定主题的通信模式,而不是仅关注一般讨论模式。该技术通过使法医分析师能够专注于语料库最有趣的部分来补充现有的可视化技术。

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