Application of BERT Model for Unsupervised Text Classification using Hierarchical Clustering for Automatic Classification of Thesis Manuscript

Many current studies on natural language processing (NLP) depend on supervised learning, which needs a lot of labeled data. This is not practical for large classification tasks. This research presents an unsupervised approach to automatically classify unlabeled theses using a BERT-hierarchical model. This technique combines BERT, an open-source machine-learning tool for NLP, with divisive hierarchical clustering utilizing the K-means clustering algorithm. Standard metrics evaluated the model's performance. Fine-tuning the BERT-hierarchical model with a learning rate of 0.00001 gave the best results, achieving 93% accuracy, 91% precision, and 93% recall. Additionally, a web application was created to facilitate model testing, offering functions for data creation, editing, and deletion. This study shows the BERT-hierarchical model's effectiveness in classifying unlabeled data, providing a scalable solution for large NLP tasks.