对比动态主题模型性能，提出时间演化评估指标
Experimental Evaluation of Dynamic Topic Modeling Algorithms
- 设计新指标衡量主题随时间变化的稳定性与连续性
- 首次在真实社交媒体数据上系统评估多类动态主题模型
- 适合关注时序文本分析与模型评估的研究者
社交媒体每日生成海量文本，分析这些内容对诸多应用具有重要意义。要理解海量文本背后的深层结构，需依赖可靠高效的自驱动主题模型。然而当前对这类模型的定量比较仍较为匮乏。本研究系统比较了多种动态主题模型，并提出一种评估指标，用于量化主题随时间演变的过程与规律，为模型选择与优化提供依据。
原文摘要 · Abstract (English)
The amount of text generated daily on social media is gigantic and analyzing this text is useful for many purposes. To understand what lies beneath a huge amount of text, we need dependable and effective computing techniques from self-powered topic models. Nevertheless, there are currently relatively few thorough quantitative comparisons between these models. In this study, we compare these models and propose an assessment metric that documents how the topics change in time.
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