Latent Semantic Analysis (LSA) Algorithm for Text Semantic Analysis

This documentation presents the Latent Semantic Analysis (LSA) algorithm for text semantic analysis, complete with detailed function specifications, principle analysis, and supporting data. The implementation includes core functions for term-frequency matrix construction, Singular Value Decomposition (SVD) processing, and semantic space dimensionality reduction. Compared to previous LSA versions, we have added demo.m file that demonstrates practical usage through visualization plots showing document similarity metrics and semantic space projections. This enhancement helps users better understand the algorithm's application while providing clearer insights into its advantages through cosine similarity calculations and dimensionality reduction visualizations. We believe this updated LSA implementation with its improved code structure and visualization components will better assist researchers in text semantic analysis tasks while more effectively meeting user requirements. For any questions or suggestions regarding the code implementation or algorithm parameters, please feel free to contact us - we're always ready to provide technical support and assistance.