The Stor-e-Motion Visualization for Topic Evolution Tracking in Text Data Streams

Andreas Weiler, Michael Grossniklaus, M. Scholl
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引用次数: 7

Abstract

Nowadays, there are plenty of sources generating massive amounts of text data streams in a continuous way. For example, the increasing popularity and the active use of social networks result in voluminous and fastflowing text data streams containing a large amount of user-generated data about almost any topic around the world. However, the observation and tracking of the ongoing evolution of topics in these unevenly distributed text data streams is a challenging task for analysts, news reporters, or other users. This paper presents “Store-Motion” a shape-based visualization to track the ongoing evolution of topics’ frequency (i.e., importance), sentiment (i.e., emotion), and context (i.e., story) in user-defined topic channels over continuous flowing text data streams. The visualization supports the user in keeping the overview over vast amounts of streaming data and guides the perception of the user to unexpected and interesting points or periods in the text data stream. In this work, we mainly focus on the visualization of text streams from the social microblogging service Twitter, for which we present a series of case studies (e.g., the observation of cities, movies, or natural disasters) applied on real-world data streams collected from the public timeline. However, to further evaluate our visualization, we also present a baseline case study applied on the text stream of a fantasy book series.
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文本数据流中主题演变跟踪的存储-运动可视化
目前,有大量的数据源以连续的方式生成大量的文本数据流。例如,社交网络的日益普及和积极使用导致了大量快速流动的文本数据流,其中包含关于世界各地几乎任何主题的大量用户生成数据。然而,在这些不均匀分布的文本数据流中观察和跟踪主题的持续演变对分析师、新闻记者或其他用户来说是一项具有挑战性的任务。本文提出了“Store-Motion”,一种基于形状的可视化技术,用于在连续流动的文本数据流上跟踪用户自定义主题通道中主题频率(即重要性)、情感(即情感)和上下文(即故事)的持续演变。可视化支持用户保持对大量流数据的概述,并引导用户感知文本数据流中意想不到的和有趣的点或周期。在这项工作中,我们主要关注来自社交微博服务Twitter的文本流的可视化,为此我们提出了一系列案例研究(例如,对城市、电影或自然灾害的观察),这些案例研究应用于从公共时间轴收集的真实数据流。然而,为了进一步评估我们的可视化,我们还提出了一个应用于幻想系列书籍文本流的基线案例研究。
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