Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning
2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE)(2023)
摘要
This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified.
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关键词
sentiment analysis,Naïve Bayes,SVM,LSTM,crime-intent,criminal,cyberbullying
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