Optimize Searching Using Latent Dirichlet Allocation - E. Ignaco, Mary Ann; Ballera, Melvin A. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
This research focuses on topic modeling as a crucial method for exploring extensive document collections and uncovering latent topics within the data. Specifically, it highlights the Latent Dirichlet Allocation (LDA) algorithm from the perspective of natural language processing. The main objective is to gain an in-depth understanding of LDA algorithms and their implementation approaches. It covers dataset preparation, practical implementation of LDA, and exploring the potential benefits of integrating LDA into recommendation systems. A comprehensive analysis of LDA encompasses graphical representations, fundamental equations, optimization methodologies, and practical implementations, covering all procedural aspects. LDA analysis is performed on extracted verses from the King James version of the Bible as documents, revealing varying levels of topic associations across documents. Some documents show strong alignment with a single topic, while others have multiple topic assignments. The research findings reveal patterns and themes present in the data, providing valuable insights into the fundamental thematic composition of the analyzed documents. Additionally, the study contributes to existing knowledge by exploring the functionality, practical applications, and relevance of topic modeling and LDA in recommendation systems and natural language processing.