Automated Email Sentiment Detection Framework

Abstract

Email has become one of the most important communication methods for individuals, educational institutions, and organizations. As the number of emails increases, understanding the emotional tone of messages becomes useful for improving communication and prioritizing responses. This project presents an Email Sentiment Analysis System that automatically identifies the sentiment expressed in email content. The system is developed using Python, Django, Natural Language Processing (NLP), and the VADER sentiment analysis library. It classifies emails into different sentiment categories such as Extremely Happy, Happy, Positive, Neutral, and Negative. Users can analyze both individual email files and multiple emails from a folder through a simple web interface. The application also provides user registration, login, and secure email management features. By automatically detecting the sentiment of emails, the system helps users understand the emotional context of messages quickly and efficiently. This approach reduces manual effort, improves productivity, and supports better decision-making in email communication.