用约束非负矩阵分解挖掘冷门主题，无需专家预设细分。
Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
- 通过种子词和流行度约束，自动学习冷门主题
- 在合成数据上优于基线模型，主题纯度与信息量更高
- 适合研究罕见但重要的议题，如网络评论中的心理健康
主题模型常难以捕捉低频但关键的冷门主题，如在线评论中的心理健康议题。现有方法虽可引入领域知识，但往往需要专家预先指定详细主题划分，限制了新主题发现。本文提出一种特殊约束的非负矩阵分解（NMF）方法，仅需提供表征冷门主题的种子词列表，无需预设主题间的具体划分。通过在冷门主题的流行度及跨主题种子词内容上施加约束，模型可学习到数据驱动的冷门主题与主流主题。该方法基于KKT条件，采用乘法更新求解。在合成数据上，模型在主题纯度、归一化互信息等指标上优于多个基线；并通过雅可比-申农散度（JSD）评估主题质量。案例研究分析了YouTube vlog评论中的心理健康讨论，成功识别并揭示了相关冷门内容。
原文摘要 · Abstract (English)
Topic models often fail to capture low-prevalence, domain-critical themes, so-called minority topics, such as mental health themes in online comments. While some existing methods can incorporate domain knowledge, such as expected topical content, methods allowing guidance may require overly detailed expected topics, hindering the discovery of topic divisions and variation. We propose a topic modeling solution via a specially constrained NMF. We incorporate a seed word list characterizing minority content of interest, but we do not require experts to pre-specify their division across minority topics. Through prevalence constraints on minority topics and seed word content across topics, we learn distinct data-driven minority topics as well as majority topics. The constrained NMF is fitted via Karush-Kuhn-Tucker (KKT) conditions with multiplicative updates. We outperform several baselines on synthetic data in terms of topic purity, normalized mutual information, and also evaluate topic quality using Jensen-Shannon divergence (JSD). We conduct a case study on YouTube vlog comments, analyzing viewer discussion of mental health content; our model successfully identifies and reveals this domain-relevant minority content.
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