A Comparative Analysis of Neural Topic Modeling Techniques for Text Analysis

Abstract

Topic modeling is a vital tool in the field of text analysis, facilitating the discovery of latent thematic structures within textual data. With the advent of neural networks, novel approaches to topic modeling have emerged. This comparative analysis explores three advanced topic modeling techniques: Neural Variational Inference (NVI), Deep Generative Models, and Transformer-Based Approaches. Each method brings unique strengths and applications to the field of natural language processing. NVI combines probabilistic modeling and neural networks, offering robustness and uncertainty modeling. Deep Generative Models, exemplified by Variational Autoencoders (VAEs), excel in data generation and fine-grained recommendations. Transformer-Based Approaches, rooted in contextual awareness, provide context-aware modeling. This analysis delves into their foundations, applications, challenges, and future directions, helping researchers and practitioners make informed choices when tackling topic modeling tasks.