Seeded Poisson Factorization: leveraging domain knowledge to fit topic models
This addresses the challenge of integrating domain knowledge into topic modeling for applications like text analysis, though it is incremental as it extends an existing framework.
The paper tackles the problem of aligning topic models with pre-defined conceptual domains by introducing seeded Poisson Factorization (SPF), which incorporates domain knowledge through seed words, resulting in superior computational efficiency and classification performance on datasets like Amazon customer feedback.
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.