A Comparative Analysis of Machine Learning Models Used for Hate Speech (Hs) Detection of Odia Language
Keywords:
component, formatting, style, styling, recall, precision, f1 score, accuracyAbstract
Social media has changed the way of communication and interaction around the world. However, hostile content can be readily exposed by the internet community due to the growth and obscurity of online media. The persons who are spreading unpleasant or hateful posts to a group of people based on their gender, political stance, color, ethnicity, or any other characteristic must be identified, either individually or as a collective. Odia, one of the six classical Indo-Aryan languages of India, is spoken by 82% of Odisha's native speakers and the remaining 18% of people who live in the states of West Bengal, Jharkhand, Andhra Pradesh, and Chhattisgarh. Due to a lack of resources, relatively little research has been done in the literature on the detection of hate speech (HS) in the Odia language. This paper's primary goal is to compile posts and comments from social media pages into an HS dataset for the Odia language. There are two categories for this dataset: HS and non-HS. Feature extraction methods and machine learning classification algorithms can be used with this dataset to identify the HS patterns from a certain social media post. Here, a comparative analysis is carried out by using this generated dataset of HS to train several machine learning models. The models' performance is compared using several metrics, including F1-score, accuracy, precision, and recall.
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