Leveraging Sentiment and Emotion Analysis to Enhance Cyberbullying Detection

Cyberbullying has emerged as a critical concern in the age of social media, where anonymity and widespread access facilitate abusive behaviors. This paper explores the effectiveness of advanced machine learning techniques combined with sentiment and emotion analysis for cyberbullying detection. We utilized a dataset of tweets and evaluated various models, including Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, to identify the most effective approaches. Our proposed model, which integrates TF-IDF with sentiment and emotion scores, achieved a high accuracy of 0.9890, outperforming established models such as those based on Random Forest with GloVe and advanced methods like RoBERTa with GloVe and PCA. Our analysis further revealed distinct emotional patterns associated with different categories of cyberbullying, with negative emotions such as anger, disgust, and fear being predominantly linked to cyberbullying content. In contrast, non-cyberbullying content displayed a more balanced emotional profile, exhibiting higher values for neutral and positive emotions. These findings underscore the significant role of emotional and sentiment analysis in enhancing the detection of harmful behaviors in online environments.