In the last decade, there has been an exponential growth of asynchronous online conversations (e.g. blogs), thanks to the rise of social media. Analyzing and gaining insights from such discussions can be quite challenging for a user, especially when the user deals with hundreds of comments that are scattered around multiple different conversations. A promising solution to this problem is to automatically mine the major topics from conversations and organize them into a hierarchical structure. However, the resultant topic hierarchy can be noisy and/or it may not match the user’s current information needs. To address this problem, we introduce a novel human-in-the-loop approach that allows the user to revise the topic hierarchy based on her feedback. We incorporate this approach within a visual text analytics system that helps users in analyzing and getting insights from conversations by exploring and revising the topic hierarchy. We evaluated the resulting system with real users in a lab-based study. The results from the user study, when compared to its counterpart that does not support interactive revisions of a hierarchical topic model, provide empirical evidence of the potential utility of our system in terms of both performance and subjective measures. Finally, we summarize generalizable lessons for introducing human-in-the-loop computation within a visual text analytics system.

中文翻译：

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用于探索在线对话集合的交互式主题层次结构修订

在过去十年中，由于社交媒体的兴起，异步在线对话（例如博客）呈指数级增长。对用户而言，分析并从此类讨论中获得见解可能非常具有挑战性，尤其是当用户处理分散在多个不同对话中的数百条评论时。这个问题的一个有前途的解决方案是自动从对话中挖掘主要主题并将它们组织成层次结构。然而，由此产生的主题层次可能是嘈杂的和/或它可能与用户当前的信息需求不匹配。为了解决这个问题，我们引入了一种新颖的人在循环方法，允许用户根据她的反馈修改主题层次结构。我们将这种方法整合到一个可视化文本分析系统中，该系统通过探索和修改主题层次结构来帮助用户分析和从对话中获取见解。我们在一项基于实验室的研究中与真实用户一起评估了生成的系统。与不支持分层主题模型的交互式修订的对应物相比，用户研究的结果提供了我们系统在性能和主观测量方面的潜在效用的经验证据。最后，我们总结了在可视化文本分析系统中引入人工在环计算的通用经验教训。与不支持分层主题模型的交互式修订的对应物相比，提供我们系统在性能和主观测量方面的潜在效用的经验证据。最后，我们总结了在可视化文本分析系统中引入人工在环计算的通用经验教训。与不支持分层主题模型的交互式修订的对应物相比，提供我们系统在性能和主观测量方面的潜在效用的经验证据。最后，我们总结了在可视化文本分析系统中引入人工在环计算的通用经验教训。