BrandSense: Machine Learning Framework for Context-Aware Brand Name Analysis and Prediction

Abstract

Establishing a strong and distinctive brand identity is essential for business success; however, generating innovative, meaningful, and market-ready brand names remains a complex, subjective, and time-intensive process. This project presents an AI-powered platform designed to automate and optimize brand name generation and evaluation by leveraging advanced Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system incorporates a robust text-processing pipeline that includes tokenization, lemmatization, and feature extraction using transformer-based embeddings such as DistilRoBERTa, enabling the capture of deep semantic relationships within textual data. To ensure high predictive performance and reliability, the platform integrates multiple classification models, including Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machines (SVM). These models are trained on datasets balanced using the Synthetic Minority Over-sampling Technique (SMOTE), improving classification accuracy and handling class imbalance effectively. The platform is supported by an interactive Web User Interface (WUI) that facilitates both single and batch predictions, providing users with suitability scores and category classifications for generated brand names. Additionally, integrated visualization tools enhance model interpretability, allowing users to analyze performance metrics and gain insights into decision-making processes. By combining state-of-the-art NLP methods with scalable ML pipelines, this system offers a data-driven solution that minimizes manual effort and subjectivity, ultimately enabling businesses to develop impactful and competitive brand identities in rapidly evolving market environments.