Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data
원문 초록
Topic models are widely used in studying social phenomena. We conduct a comparative study examining state-of-the-art neural versus non-neural topic models, performing a rigorous quantitative and qualitative assessment on a dataset of tweets about the COVID-19 pandemic. Our results show that not only do neural topic models outperform their classical counterparts on standard evaluation metrics, but they also produce more coherent topics, which are of great benefit when studying complex social problems. We also propose a novel regularization term for neural topic models, which is designed to address the well-documented problem of mode collapse, and demonstrate its effectiveness.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
Evaluating Negative Sampling Approaches for Neural Topic Models
Seeded Poisson Factorization: Leveraging domain knowledge to fit topic models
Refining Dimensions for Improving Clustering-based Cross-lingual Topic Models
Locating the Leading Edge of Cultural Change
토론
이 논문과 연결된 포럼 글이 아직 없습니다.