Article
RoBERTa-Based Deep Learning Approach for Industry and Brand Classification Systems
Industry and brand classification was earlier done using manual taxonomies and rule-based 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 bi-directional encoder representation with transformers pretrained approach (RoBERTa) is one such model that reads text with better understanding. In this research, Robustly optimised bi-directional 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 transformer-based Natural Language Processing (NLP). This research leverages Robustly optimised Bi-directional Encoder Representation with Transformers pretrained approach (RoBERTa), a state-of-the-art transformer model, fine-tuned on labeled product data collected from titles, descriptions, and reviews. The system processes raw text through cleaning, tokenization, and multi-task 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
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