Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA

This research compares unsupervised learning methods in topic extraction and modeling large-scale text corpora. The used are Singular Value Decomposition (SVD) Latent Dirichlet Allocation (LDA). SVD is to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. involves data collection, exploratory analysis (EDA), using SVD, preprocessing, LDA. were Data explorative was conducted understand characteristics structure corpora before performed. identify main results showed that successful reveals cohesive patterns thematically related topics. These findings have implications processing analysis. resulting representations can be for information mining, document categorization, more in-depth use provides valuable insights However, this has limitations. success depends quality representativeness Topic interpretation still requires further understanding Future develop techniques improve accuracy efficiency modeling.