Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their correlations as edges. Further details are displayed interactively to support the exploration of large text collections, e.g., representative words and sentences of topics, topic and sentiment distributions, hierarchical topic clustering, and customizable, predefined topic labels. The toolkit optimizes automatically on custom data for optimal coherence. We show a working instance of the toolkit on data crawled from English social media discussions about organic food consumption. The visualization confirms findings of a qualitative consumer research study. SocialVisTUM and its training procedures are accessible online.

翻译：近期舆论挖掘研究提出了基于词嵌入的主题建模方法，相较于传统主题建模具备更优的一致性。本文展示了如何利用我们提出的交互式可视化工具包SocialVisTUM，将这些方法应用于社交媒体文本的相关主题模型展示。该工具包以节点表示主题、边表示主题关联的图谱形式呈现。通过交互式细节展示（如主题代表性词语与句子、主题与情感分布、层次化主题聚类、可定制的预定义主题标签），支持大型文本语料库的探索性分析。工具包可针对自定义数据自动优化以实现最佳一致性。我们以爬取自有机食品消费英文社交媒体讨论的数据为例展示工具包的实际运行效果，其可视化结果验证了定性消费者研究结论。SocialVisTUM及其训练流程可通过在线渠道获取。