SOCIAL THREAT IDENTIFICATION ON X VIA SENTIMENT ANALYSIS: AN SVM AND LSTM-BASED APPROACH

Abstract

 This research explores the use of tweet sentiment analysis for threat detection and identification based on X data. X provides a valuable platform for monitoring public sentiment and identifying potential dangers in real time. By categorizing tweets as positive, negative, or neutral, analysts can gain insights into public opinion and identify potential risks related to specific events or topics. The research reviews prior work on using Tweet sentiment analysis for threat detection, highlighting the advantages, challenges, and limitations. It identifies research gaps in the field and proposes a comprehensive approach that combines sentiment analysis techniques with real-time data gathering to identify and mitigate threats with minimal delay.