Topic Modelling using Transfer Learning: Issues and Challenges

A machine learning method, transfer learning, uses information from one job or area to enhance effectiveness in a related but distinct task or area. Transfer learning enables using pre-trained models that were previously learned using a sizable and varied dataset in the original domain instead of creating a model from the beginning for the intended activity. Usually, using a lesser dataset, the pre-trained model's representations and knowledge may be improved upon or applied to the intended goal or area. In natural language processing (NLP) and machine learning, topic modeling is an approach that finds and extracts the fundamental concepts or subjects from a group of documents or textual data. Despite prior experience with the aspects, this unsupervised learning method seeks to uncover the text's latent semantic structure. This paper's primary goal is to investigate the possibilities of transfer learning for text topic modeling. Additionally, to assess the correctness of the pre-trained model's performance. The paper also describes the issues and challenges associated with the text for performing the topic modeling using transfer learning. This paper delves into the methodologies and techniques of topic modeling using transfer learning. It explores how this novel approach transcends the constraints of conventional topic modeling, enabling the extraction of meaningful topics from data-rich source domains to improve topic modeling performance in data-scarce or specialized target domains. By integrating the principles of transfer learning into the topic modeling framework, we unlock the potential to enhance the interpretability, accuracy, and applicability of topic modeling across diverse domains and applications. This paper sheds light on the transformative power of transfer learning in topic modeling.