AI-Powered Brand Identity Modeling through Intelligent Name Generation and Market Evaluation

Abstract

Establishing a strong and distinctive brand identity is a key factor in gaining a competitive edge, but creating original, meaningful, and market-relevant brand names is often a complex and time-consuming task. Conventional approaches primarily depend on human imagination, brainstorming sessions, and rule-based strategies, which can be subjective, inconsistent, and limited in their ability to recognize complex semantic relationships or adapt to evolving market trends. To address these challenges, this work introduces an AI-powered framework that combines natural language processing (NLP) with machine learning to automate the generation and assessment of brand names. The proposed methodology starts with comprehensive text preprocessing, including data cleaning, tokenization, lemmatization, and stop-word elimination to produce highquality textual inputs. It then employs transformer-based language models, such as DistilRoBERTa, to generate contextual embeddings that effectively capture semantic and linguistic information from words and phrases. For classification, several machine learning techniques, including Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM), are trained using balanced datasets, where the Synthetic Minority Over-sampling Technique (SMOTE) is utilized to mitigate class imbalance. The developed system supports both individual and bulk brand name analysis, providing outputs such as predicted brand categories, relevance scores, and evaluation metrics through an intuitive web-based interface. By integrating advanced NLP capabilities with reliable machine learning algorithms, the proposed solution enhances scalability, efficiency, and prediction accuracy while minimizing manual effort, reducing bias, and accelerating the creation of effective, distinctive, and market-oriented brand names.