Classification of Offensive Tweet in Marathi Language Using Machine Learning Models

Offensive language identification is essential to make social media a safe and clean place to share one’s view. In this work, a model is proposed to automatically classify offensive tweets into offensive and not offensive classes of low-resource language. Marathi is spoken in Western India. Marathi being a low-resource language, lacks a comprehensive list of stopwords and proper stammer. To fill this gap, we created a list of stopwords for stopword removal and a list of suffixes to identify the root word in the Marathi language. Two different methods, Label Vectorizer and term frequency-inverse document frequency (TF-IDF) Vectorizer, are used to extract features from the text and then these features are used with six different conventional machine learning classifiers to classify a Marathi tweet into offensive or non-offensive.