INERA – intelligent email reply assistant to reduce email overload based on deep learning

Abstract

Email overload is a pressing concern in modern workplaces and businesses, as it hampers productivity and efficiency. Repetitive tasks, such as responding to customer inquiries, contribute to this escalating issue. Previous literature has investigated various methods to mitigate email overload, including email prioritization, structuring, missing attachment prediction, reply suggestion, keyword summarization, and task scheduling. Despite these efforts, existing solutions exhibit notable limitations, such as reliance on manual user validation and generating responses that lack comprehensiveness and context. In this research, we developed InERA – Intelligent Email Reply Assistant, a deep learning-based solution designed to alleviate email overload. InERA generates comprehensive, context-aware responses to general inquiries by leveraging insights from previous similar emails handled by the user. The system employs a classification module to identify responses that match the current email intent, utilizing natural language processing techniques and machine learning algorithms, such as k-means clustering, Multi-Layer Perceptron classifier, and LSTM model. InERA generates meaningful replies by considering previous user responses and email thread information, ultimately reducing the need for manual intervention. The thesis is structured with chapters on Introduction, Literature Review, Technology Adoption, Approach, Design, Implementation, Evaluation, and Conclusion. These chapters detail the development, implementation, and evaluation of InERA, including its user interface design, integration with email clients, and evaluation through user testing and performance metrics. By implementing InERA, organizations can alleviate the burden of email overload, significantly improving productivity and fostering more efficient communication. Furthermore, this research highlights the broader implications and potential future enhancements of the automatic email reply assistance system in various industries and academic fields, emphasizing ethical considerations and privacy concerns.