Unsupervised Sinhala Cyberbullying Categorization

Abstract

The objective of unsupervised machine learning is to categorize the social media comments into a given number of pre-learned categories. The earlier studies of this domain have used many the dataset for supervised learning & introduced a large number of techniques, methodologies. A major challenge there was training labels. Although words with training comments are easy to find, separating them manually is not an easy task. Through this research, we hope to find a solution to this using unsupervised machine learning techniques. the proposed technique divides the comments into words and removed special characters, emojis, and links from the comments & categorized each comment using a keyword list of each category and similarity findings. And then this was used to categorize comments for training. The implemented method shows the same performance, by Comparison with other supervised machine learning techniques for cyberbullying. Therefore, this mechanism can be used in any other places where low-cost cyberbullying identification is needed. This also can be used to create train comments.