YouTube spam classification using n-gram

Isahak, Nurul Aqilah (2020) YouTube spam classification using n-gram. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Social networking is the phase used for user interaction with others. Spam has become a trend attack and most YouTube users do not know and not aware of spam attacks. Unsolicited bulk mail known as spam has become one of the most Internet's disruptive issues. This research uses the Support Vector Machine (SVM) to develop a YouTube detection framework. The dataset will be used is YouTube spam dataset obtained from the UCI Machine Learning Repository website which is this data most frequently used by the previous researcher. This dataset process through Bag-of-Word, Chi-Square, Information Gain and propose an SVM model that produces from this research. The objective of this project is to prove that SVM can provide result accuracy in spam comments on YouTube. This research is giving hope to produce a system that can detect spam and legitimate comment on YouTube methods based on the SVM model.

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