Gender Abusive Language Detection in Bengali using Machine Learning algorithms

The issue of gender-based abuse is a prevalent concern in today’s society. As technology and social media platforms have become increasingly ubiquitous, these platforms have also become a breeding ground for abusive language and harassment, particularly toward women. In this study, we utilized machine learning techniques—logistic regression, decision tree, random forest, K-nearest neighbors, support vector machine, and Naïve Bayes—to classify abusive text based on gender. The considered dataset in this research comprised comments and posts from various social media platforms, which were preprocessed before being subjected to classification. Experimental analysis revealed that support vector machine demonstrated superior performance in terms of precision, recall, accuracy, sensitivity, and specificity indicating its potential effectiveness in identifying and filtering out gender-based abuse from social media platforms. The findings of this study suggest that machine learning techniques can play a critical role in combating gender-based abuse and harassment online.