• Taralkumar Contractor
• Mr. Jitendrakumar Dhobi
• Mr. Nitin Raval

Latent Semantic Analysis (LSA) is a potent tool aimed at bridging the gap between human language comprehension and machine understanding. Recognizing that language extends beyond mere words and faces challenges of high dimensionality in textual data, LSA operates on the premise that documents and words sharing similar contexts likely have semantic connections. This paper delves into the foundational principles of LSA, its applications, and its impact across various domains. LSA is a fully automated mathematical approach that extracts and infers relationships among words' expected contextual usage, utilizing techniques such as Document Term Matrix and Singular Value Decomposition. It serves as a versatile method for comprehending and learning from text, providing valuable insights to enhance information retrieval and filtering. As the field of Natural Language Processing progresses, LSA remains a fundamental technique for extracting insights from textual data.

Latent Semantic Analysis, Document Term Metrix, Singular Value Decomposition

"Latest Trends in Latent Semantic Analysis Method and Comprehensive Preview", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i114-i122, April-2024, Available :http://www.jetir.org/papers/JETIR2404816.pdf

"Latest Trends in Latent Semantic Analysis Method and Comprehensive Preview", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi114-i122, April-2024, Available at : http://www.jetir.org/papers/JETIR2404816.pdf