Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with pre-defined conceptual domains. This paper introduces seeded Poisson Factorization (SPF), a novel approach that extends the Poisson Factorization (PF) framework by incorporating domain knowledge through seed words. SPF enables a structured topic discovery by modifying the prior distribution of topic-specific term intensities, assigning higher initial rates to pre-defined seed words. The model is estimated using variational inference with stochastic gradient optimization, ensuring scalability to large datasets. We present in detail the results of applying SPF to an Amazon customer feedback dataset, leveraging pre-defined product categories as guiding structures. SPF achieves superior performance compared to alternative guided probabilistic topic models in terms of computational efficiency and classification performance. Robustness checks highlight SPF's ability to adaptively balance domain knowledge and data-driven topic discovery, even in case of imperfect seed word selection. Further applications of SPF to four additional benchmark datasets, where the corpus varies in size and the number of topics differs, demonstrate its general superior classification performance compared to the unseeded PF model.

翻译：主题模型被广泛用于发现大规模文本语料库中的潜在主题结构，然而传统的无监督方法往往难以与预定义的概念领域对齐。本文提出种子泊松分解（SPF），这是一种通过种子词融入领域知识、扩展泊松分解（PF）框架的新方法。SPF通过修改主题特定词项强度的先验分布，为预定义的种子词分配更高的初始权重，从而实现结构化主题发现。该模型采用随机梯度优化的变分推断进行估计，确保了对大规模数据集的可扩展性。我们详细展示了将SPF应用于亚马逊客户反馈数据集的结果，利用预定义的产品类别作为引导结构。在计算效率和分类性能方面，SPF相较于其他引导式概率主题模型均表现出更优性能。稳健性检验表明，即使在种子词选择不完善的情况下，SPF仍能自适应地平衡领域知识与数据驱动的主题发现。将SPF进一步应用于四个额外基准数据集（其语料库规模各异、主题数量不同）的结果显示，相较于无种子词的PF模型，SPF在分类性能上具有普遍优越性。