{'fraction_non_alphanumeric': 0.021986353297952996, 'fraction_numerical': 0.003032600454890068, 'mean_word_length': 4.972850678733032, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hate and Offensive Speech Detection in Hindi Twitter Corpus

Nowadays, social media sites like Twitter and Facebook emerge as user-friendly and accessible sources for people to express their voice. Everybody, irrespective of their age group, uses these sites to share every moment of their life, making these sites flooded with data. This has led to many positive outcomes. At the same time, it has brought risks and harms as these sites set no restrictions. The volume of hate speech is not manageable by humans. As part of the HASOC-2021 shared task on information retrieval, we, Team Ignite, address the problem of hate speech identification in the Hindi corpus. Subtask A aims to identify binary hate or non-hate speech. This work was further extended with subtask B to determine the result of subtask A into three categories: profane, offensive, and hate. Hence, this paper compares the performance of three feature engineering techniques and four machine learning algorithms to evaluate their performance on a publicly available dataset with two distinct classes. With these two classes of hate and non-hate, we create a baseline model and improve model performance scores using various optimization techniques. Moreover, the output of different comparisons can be used further for text classification techniques.