Email Sentiment Analysis for Fraud detection

What is it about?

As the volume of emails continues to rise, it has become increasingly crucial to implement effective spam filters and manage the influx of messages efficiently. To this end, a study was conducted using natural language processing and machine learning techniques to analyze the sentiments conveyed in emails. By categorizing emails based on positive, negative, or neutral sentiments, the study aimed to improve email management. The results revealed that the Random Forest machine learning method was the most effective in accurately classifying emails. Overall, this research contributes to enhancing email management practices, enabling users to prioritize and respond to messages more effectively.

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Photo by Growtika on Unsplash

Photo by Growtika on Unsplash

Why is it important?

The research presented here is unique in that it focuses on email classification through the use of sentiment analysis. While email classification is a well-established area of research, this study specifically addresses the challenge of classifying emails based on their sentiment, which is particularly relevant for fraudulent detection. By applying natural language processing and machine learning techniques, this research offers a novel approach to automatically categorize emails as positive, negative, or neutral, thereby enabling efficient routing and response handling. Furthermore, the study compares multiple machine learning methods to determine the most effective approach for email classification, providing valuable insights into algorithmic performance. In summary, the combination of sentiment analysis, email classification, and machine learning techniques presented in this research contributes to the advancement of email management and email fraudulent detection.

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