Abstract
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.
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Kumari, A., Garge, A., Raj, P., Kumar, G., Singh, J.P., Alryalat, M. (2024). Classification of Offensive Tweet in Marathi Language Using Machine Learning Models. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_20
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