The aim of this study is to evaluate the quality of topic solutions generated by Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), and fuzzy Latent Semantic Analysis (fLSA). By introducing the CL, RL, and HO indices, the study focuses on structural properties such as oversimplification, redundancy, and homogeneity, offering a novel approach to complement traditional metrics like coherence and perplexity. This framework provides a nuanced perspective for assessing topic quality.
Low-Rank Analysis of Topic Quality: Comparing LDA, CTM, and Fuzzy-LSA methods
antonio calcagni'
2025
Abstract
The aim of this study is to evaluate the quality of topic solutions generated by Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), and fuzzy Latent Semantic Analysis (fLSA). By introducing the CL, RL, and HO indices, the study focuses on structural properties such as oversimplification, redundancy, and homogeneity, offering a novel approach to complement traditional metrics like coherence and perplexity. This framework provides a nuanced perspective for assessing topic quality.
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