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Empirical evidences linking users’ psychological features such as personality traits and cybercrimes such as cyberbullying are many. This study deals with automatic cyberbullying detection mechanism tapping into Twitter users’ psychological features including personalities, sentiment and emotion. User personalities were determined using Big Five and Dark Triad models, whereas machine learning classifiers namely, Naïve Bayes, Random Forest and J48 were used to classify the tweets into one of four categories: bully, aggressor, spammer and normal. The Twitter dataset contained 5453 tweets gathered using the hashtag #Gamergate, and manually annotated by human experts. Selected Twitter-based features namely text, user and network-based features were used as the baseline algorithm. Results show that cyberbullying detection improved when personalities and sentiments were used, however, a similar …