Abstract
Cyber-aggression and misogynistic aggression is a form of abusive text that has been risen on various social media platforms. Any type of cyber-aggression is harmful to society and social media users, and it also provokes hate crimes. Misogynistic aggression shows hatred toward women. Automatic detection models for hateful tweets can combat this problem. Contextual analysis of tweets can improve existing detection models. We propose a novel syntactic and semantic feature-based classification model where paragraph embedding enriches the contextual analysis of aggressive tweets and TF-IDF identifies the importance of each term in tweet and corpus. This study considers Trac-2 shared task English language dataset for multitasking classification. This novel research shows achievable performance against a baseline model and various classifiers. Among the various machine learning and deep learning classifiers, multilayer perceptron (MLP) portrays the preeminent performance.
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Ghosal, S., Jain, A. (2023). Analysis of Misogynistic and Aggressive Text in Social Media with Multilayer Perceptron. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_51
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