Analyzing Abusive Bangla Comments on Social Media: NLP & Explainable AI

Popularity of Social media sites has been on the rise ever since its invention. Due to the anonymity the social media sites provide to the users, an influx of divergent behavior has also been increasing. Abusive language and offensive content are posted on these sites to ridicule others which creates a harmful environment for the people affected. Detecting these misconducts, especially for Bengali Language is still vague. Our paper tries to bridge the gap between the abusive content detection using Natural Language Processing with five different machine learning classifiers such as Decision Tree, Random Forest, Multinomial Naïve Bayes, Support Vector Classifier and Logistic Regression and give clarity on the factors that contribute to the prediction using Explainable AI. The dataset was preprocessed in multiple steps including stop words and punctuation removal, null and duplication value check and tokenization. The dataset was then processed to created TF-IDF representations with n-grams. Grid Search Cross Validation technique was used to find out optimal combination of hyperparameters for our machine learning models. Among all the models Logistic Regression performed the best with an accuracy of 85.57%. For transparency and clarification of the model's detection methodologies Lime model was used.