Analyzing and understanding Internet news are important for many applications, such as market sentiment investigation and crisis management. However, it is challenging for users to interpret a massive amount of unstructured text, to dig out its accurate meaning, and to spot noteworthy news events. To overcome these challenges, we propose a novel visualization-driven approach for analyzing news text. We first collect Internet news from different sources and encode sentences into a vector representation suitable for input to a neural network, which calculates a sentiment score, to help detect news event patterns. A subsequent interactive visualization framework allows the user to explore the development of and relationships between Internet news topics. In addition, a method for detecting news events enables users and domain experts to interactively explore the correlations between market sentiment, topic distribution, and event patterns. We use this framework to provide a web-based interactive visualization system. We demonstrate the applicability and effectiveness of our proposed system using case studies involving blockchain news.

中文翻译：

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通过情感评分和主题模型对Internet新闻进行可视化探索

分析和理解Internet新闻对于许多应用程序都很重要，例如市场情绪调查和危机管理。但是，用户要解释大量的非结构化文本，挖掘其准确含义并发现值得注意的新闻事件是一项挑战。为了克服这些挑战，我们提出了一种新颖的可视化驱动的方法来分析新闻文本。我们首先从不同来源收集互联网新闻，然后将句子编码成适合于输入到神经网络的矢量表示形式，该神经网络会计算情感得分，以帮助检测新闻事件模式。随后的交互式可视化框架允许用户探索Internet新闻主题的发展以及它们之间的关系。此外，一种用于检测新闻事件的方法，使用户和领域专家可以交互式地探索市场情绪，主题分布和事件模式之间的相关性。我们使用此框架来提供基于Web的交互式可视化系统。我们通过涉及区块链新闻的案例研究证明了我们提出的系统的适用性和有效性。