Email Categorization and Prioritization using Machine Learning and Deep Learning

Abstract

"Email overload is a challenge in today's digital world. This dissertation explores the development of an application that leverages machine learning to prioritize emails based on their content. The application utilizes BERT-Uncased, a transfer learning technique, to analyse email text and assign priority labels. By integrating with the Gmail API, the application can automatically download emails, categorize them based on importance, and update labels within Gmail. The project prioritizes novelty by addressing the gap in research on content-based email prioritization. It tackles the challenge of limited data by employing BERT-Uncased, which is efficient for working with smaller datasets. The chosen research methodology involves transfer learning and API integration, balancing effectiveness with real-world feasibility. The proposed solution offers a viable approach for email prioritization, requiring less data compared to traditional methods. However, limitations exist: Using smaller datasets might introduce bias, and the application could be generalized by incorporating more diverse datasets. This thesis contributes to the field by demonstrating the potential of machine learning for email management. It highlights the effectiveness of transfer learning techniques like BERT-Uncased for practical applications with limited data. The evaluation considers perspectives from domain experts, technical specialists, and target users, providing a well-rounded assessment of the project's strengths, weaknesses, and future directions."