Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert.

中文翻译：

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StanceVis Prime：社交媒体文本中情绪和立场的视觉分析

文本可视化和可视化文本分析方法已成功应用于与分析单个文本文档和大型文档集合相关的各种任务，例如主要主题的摘要或话语中的事件识别。在文本数据中检测到的情绪和情绪的可视化也成为一个重要的话题，特别是对于源自社交媒体的数据。尽管对该主题的兴趣越来越大，但现有方法尚未充分解决与检测和可视化各种立场（例如粗鲁或不确定）相关的研究问题。与此问题相关的挑战包括底层计算方法的开发和相应多标签立场分类结果的可视化。在本文中，我们描述了我们在名为 StanceVis Prime 的可视化分析平台上的工作，该平台旨在分析来自各种社交媒体数据源的时间文本数据中的情绪和立场。StanceVis Prime 的用例场景包括社交媒体监控和社会语言学研究。该设计的动机是语言学领域合作专家的要求，作为立场分析的大型研究项目的一部分。我们的方法涉及使用来自多个文本流源的文档并应用情绪和立场分类，从而产生与源文本相关联的多个数据系列。StanceVis Prime 为最终用户提供基于动态时间扭曲分析的数据系列之间的相似性概览，以及数据系列值的详细可视化。用户还可以检索和进行与数据系列对应的文档的远距和近距阅读。我们通过涉及感兴趣的政治目标和几个社交媒体数据源的案例研究展示了我们的方法，并报告了从领域专家那里收到的初步用户反馈。