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Affective Mail: A Smart Web Application for Email Emotion Prediction
This project presents a web-based system designed to perform sentiment analysis on email data using a Python-powered backend. The application enables users to register, log in, and analyze the emotional tone of emails either individually or in bulk through a simple browser interface. By uploading a single email file or an entire folder containing multiple emails, the system processes textual content and predicts sentiment categories such as positive, negative, or extremely happy. The platform integrates an easy-to-use workflow, starting from server initialization to result visualization, making it accessible even to non-technical users. The results are displayed in a structured format where users can clearly view both the original email content and its corresponding sentiment prediction. This project demonstrates how natural language processing techniques can be effectively applied to real-world communication data, providing valuable insights for applications such as customer feedback analysis, automated email classification, and decisionmaking support systems.
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