The implementation of Latent Dirichlet Allocation (LDA) model on IEEE Xplore dataset to find the impact of artificial intelligence in education sector

Abstract

The field of education holds immense significance, calling for a reevaluation of learning methods and approaches. Particularly in recent years, there has been a growing inclination within higher education to incorporate emerging technologies and artificial intelligence (AI) in order to enrich the learning process. This study aims to analyze the Latent Dirichlet Allocation LDA, as a probabilistic Bayesian model designed for analyzing collections of discrete data, such as text corpora. LDA employs a three-level hierarchical Bayesian model, where each item in the collection is represented as a finite mixture derived from a set of underlying topics. These topics, in turn, are modeled as an infinite mixture based on a set of topic probabilities. In the realm of text modeling, the topic probabilities offer a transparent representation of a document's content. The descriptive analyze work on data collection from IEEE Xplore from 2011 to 2022 years.