K. Chiranjeevi, B. Poojitha, K. Balakrishna, N. Siva Nagamani 03 04(1) Industry and brand classification was earlier done using manual taxonomies and rulebased systems These methods were slow and needed a lot of human effort They often made mistakes with similar brand names or overlapping industries Traditional machine learning models improved speed but still needed manual feature design They could not fully understand the meaning of words in context With new deep learning methods transformers brought a big change Robustly optimised bidirectional encoder representation with transformers pretrained approach RoBERTa is one such model that reads text with better understanding In this research Robustly optimised bidirectional encoder representation with transformers pretrained approach RoBERTa taxanomy uses this model for automated industry and brand classification It reduces human work and gives more accurate results This shows how automation with NLP is better than old manual approaches Robustly optimised bidirectional encoder representation with transformers pretrained approach RoBERTa Taxonomy is an automated product classification system designed to identify the industry brand and category of any product text using transformerbased Natural Language Processing NLP This research leverages Robustly optimised Bidirectional Encoder Representation with Transformers pretrained approach RoBERTa a stateoftheart transformer model finetuned on labeled product data collected from titles descriptions and reviews The system processes raw text through cleaning tokenization and multitask classification layers that independently predict industry brand and hierarchical product categories By mapping predictions into a structured taxonomy the model provides accurate and consistent product tagging Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026