Classification using Support Vector Machine to Detect Cyberbullying in Social Media for Myanmar Language

As a growth of the technological world, web technologies and social networking emerged and played an important role in telecommunication. People misuse the social network as a new weapon to make a person attack unable to find the identity of the attacker. Due to the illegal action, the technological world seems to face new challenges and new risks like cyberbullying. This paper proposes a supervised method for detection of Myanmar cyberbullying in social media by using Support Vector Machine (SVM) classifier. The proposed method includes three main steps: data preprocessing, word segmentation, and classification. In the first step, we extract the posts written in Myanmar text from social media. We break the posts and sentences into syllables into words by using the Longest Syllable Matching approach along with a dictionary as the second step. For the third step, we apply Support Vector Machine classifier to detect cyberbullying in social media whether the bullying words or not. Consequently, the experimental result shows that our method obtains 0.7540 classification accuracy in terms of F-score.