Emotion Analysis for Scam Detection in Social Media and Communication Platforms: Using Machine Learning

P. Alagu Manohar and R. Hemamalini

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 4, 844-849

Abstract: This project would leverage sentiment analysis techniques to identify scam patterns in text data, such as emails, social media posts, or instant messages. Scammers often use emotional manipulation (e.g., fear, urgency, greed) to deceive victims. The goal is to detect potential scam messages by analysing the sentiment and identifying scam-related patterns in the text. These messages can come from various sources, such as emails, SMS, or social media. The system combines sentiment scores with scam-related features, such as urgency keywords, to classify messages. The solution aims to combat phishing and fraud in communication channels, enhancing network security. Sentiment analysis is applied to detect the overall tone of the message, while emotion classification techniques identify specific emotional cues embedded in the text. A series of machine learning models, including Random Forest are then trained to classify messages as either genuine or potentially fraudulent based on the extracted sentiment and emotion features. This paper highlights the critical role of emotion analysis in detecting scams in modern communication platforms, proposing a more robust and nuanced approach to cybersecurity. By incorporating emotional cues into scam detection systems, the proposed method offers a promising solution to combating the growing threat of online scams and enhancing user safety on social media and communication platforms. In this proposed paper, it checks the genuine emotions and identify the fraudulent text patterns to deduct scams.

Keywords: Sentiment Analysis; Network Security; Scam Detection; Phishing; Machine Learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i4:id:1082

DOI: 10.32628/IJSRST251364

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