Unsolicited Email Filtering

Spam emails, commonly known as junk emails, are unsolicited messages sent in bulk to numerous recipients. These messages typically inundate email inboxes and can range from annoying promotional content to potentially harmful phishing attempts and malware distribution. This work presents a comprehensive approach to email classification and sentiment analysis. It begins by preprocessing email data, encompassing lowercasing, punctuation removal, tokenization, and stemming. The study uses Logistic Regression, Support Vector Machine (SVM), and Naive Bayes models to classify emails as spam or ham with machine learning. The accuracy of these models is evaluated and compared, with SVM demonstrating the highest accuracy. Sentiment analysis is performed using the V alence Aware Dictionary for Sentiment Reasoning (VADER) tool to evaluate email sentiment. This work also generates word clouds to visually represent the most frequent terms in both input mail and the dataset.