Comparative Analysis of Machine Learning-Driven and Traditional Method for User Requirement Analysis in Software Development

The study aims to cope with the drawbacks incurred in the conventional User Requirement Analysis (URA) through a new framework that integrates Sentiment Analysis, clustering, and Support Vector Machine (SVM) classifiers. This approach's goal is to optimize URA by categorizing user requirements based on the sentiment of specific target websites. It also demonstrates that these approaches outperform conventional methods in terms of accuracy, recall rates, and speed, enabling more effective consideration of clients' needs in problem-solving processes. In general, this research will explore the potential of machine learning to optimise and enhance the URA process, while also highlighting potential issues that could complicate the requirement gathering process.