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1. A Multi-Stage Machine Learning and
Fuzzy Approach to Cyber
Abstract
Social media has revolutionized the way individuals connect and share
information globally. However, the rise of these platforms has led to the
proliferation of cyber-hate, which is a significant concern that has garnered
attention from researchers. To com
proposed, utilizing Machine learning and Deep learning techniques such as
Naive Bayes, Logistic Regression, Convolutional Neural Networks, and
Recurrent Neural Networks. These methods rely on a mathematical appro
to distinguish one class from another. However, when dealing with sentiment
oriented data, a more “critical thinking” perspective is needed for accurate
classification, as it provides a more realistic representation of how people
interpret online messages. Based on a literature review conducted to explore
efficient classification techniques, this study applied two machine learning
classifiers, Multinomial Naive Bayes and Logistic Regression, to four online
hate datasets. The results of the classifiers w
optimization techniques such as Particle Swarm Optimization and Genetic
Algorithms, in conjunction with Fuzzy Logic, to gain a deeper understanding of
the text in the datasets.
Stage Machine Learning and
Fuzzy Approach to Cyber-Hate Detection
Social media has revolutionized the way individuals connect and share
information globally. However, the rise of these platforms has led to the
hate, which is a significant concern that has garnered
attention from researchers. To combat this issue, various solutions have been
proposed, utilizing Machine learning and Deep learning techniques such as
Naive Bayes, Logistic Regression, Convolutional Neural Networks, and
Recurrent Neural Networks. These methods rely on a mathematical appro
to distinguish one class from another. However, when dealing with sentiment
oriented data, a more “critical thinking” perspective is needed for accurate
classification, as it provides a more realistic representation of how people
ges. Based on a literature review conducted to explore
efficient classification techniques, this study applied two machine learning
classifiers, Multinomial Naive Bayes and Logistic Regression, to four online
hate datasets. The results of the classifiers were optimized using bio
optimization techniques such as Particle Swarm Optimization and Genetic
Algorithms, in conjunction with Fuzzy Logic, to gain a deeper understanding of
Stage Machine Learning and
Hate Detection
Social media has revolutionized the way individuals connect and share
information globally. However, the rise of these platforms has led to the
hate, which is a significant concern that has garnered
bat this issue, various solutions have been
proposed, utilizing Machine learning and Deep learning techniques such as
Naive Bayes, Logistic Regression, Convolutional Neural Networks, and
Recurrent Neural Networks. These methods rely on a mathematical approach
to distinguish one class from another. However, when dealing with sentiment-
oriented data, a more “critical thinking” perspective is needed for accurate
classification, as it provides a more realistic representation of how people
ges. Based on a literature review conducted to explore
efficient classification techniques, this study applied two machine learning
classifiers, Multinomial Naive Bayes and Logistic Regression, to four online
ere optimized using bio-inspired
optimization techniques such as Particle Swarm Optimization and Genetic
Algorithms, in conjunction with Fuzzy Logic, to gain a deeper understanding of