Classification of pornographic content on Twitter using support vector machine and Naive Bayes

Keyphrases

• Support Vector Machine 100%
• Twitter 100%
• Naïve Bayes 100%
• Pornographic Content 100%
• Social Media 50%
• Bigram 50%
• F1 Score 50%
• Classification Algorithms 50%
• World Wide Web 50%
• Unigram 50%
• Feature Classification 25%
• Number of children 25%
• Fraud 25%
• Model Selection 25%
• Child Victims 25%
• Best Model 25%
• Violence 25%
• K-fold Cross-validation 25%
• Gambling 25%
• Score Value 25%
• Radicalism 25%
• Negative Content 25%
• Common Words 25%
• Racial Intolerance 25%

Computer Science

• Support Vector Machine 100%
• Classification Algorithm 66%
• Fold Cross Validation 33%
• Gain Knowledge 33%