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Identifying sarcasmSarcasm is challenging because it depends on various contextual and non-verbal information. To accurately understand the intended meaning, it’s essential to recognize sarcasm in statements, particularly on open platforms like Facebook, Twitter, WhatsApp, etc., where people express themselves freely. Sarcasm detection in Hindi languages is challenging in natural language processing (NLP)Natural Language Processing (NLP) due to the richness of annotated datasets and insufficient resources. Hindi, the fourth-most popular language in the world, remains unexplored in sarcasmSarcasm detection. An automated system is necessary for sarcasmSarcasm detectors in textual data, and considerable researchers have proposed several sarcasm detection algorithms. Since English is the most often used language in social networking groups, scholars use this language most to identify sarcasmSarcasm in written content. There is a lack of research on sarcasmSarcasm detection in various Asian languages, including Hindi, Telugu, Tamil, Urdu, and Bengali. In this paper, we suggested a model that usesEnsemble learning ensembleEnsemble machine learning (ML)Machine learning and a BERT-based-LSTM model to detect sarcasmSarcasm in Hindi text. Both models perform satisfactorily in terms of precision, recall, F-score, and accuracy, although theHindiBERT HindiBERT-LSTM (Long Term-Short Memory) model achieves higher performance overall. This model’s accuracy is 96% on average.

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Sarcasm Detection in Hindi Social Media Text Using HindiBERT-LSTM and XGBoost Models

  • 会议论文

  • 发表日期：

    2025年

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  • Y. Indira Priyadarshini,
  • K. Mahesh Babu,
  • Y. Bhargavi,
  • Y. Dharani,
  • Y. Ramya,
  • P. Mani Chandana

摘要

Identifying sarcasmSarcasm is challenging because it depends on various contextual and non-verbal information. To accurately understand the intended meaning, it’s essential to recognize sarcasm in statements, particularly on open platforms like Facebook, Twitter, WhatsApp, etc., where people express themselves freely. Sarcasm detection in Hindi languages is challenging in natural language processing (NLP)Natural Language Processing (NLP) due to the richness of annotated datasets and insufficient resources. Hindi, the fourth-most popular language in the world, remains unexplored in sarcasmSarcasm detection. An automated system is necessary for sarcasmSarcasm detectors in textual data, and considerable researchers have proposed several sarcasm detection algorithms. Since English is the most often used language in social networking groups, scholars use this language most to identify sarcasmSarcasm in written content. There is a lack of research on sarcasmSarcasm detection in various Asian languages, including Hindi, Telugu, Tamil, Urdu, and Bengali. In this paper, we suggested a model that usesEnsemble learning ensembleEnsemble machine learning (ML)Machine learning and a BERT-based-LSTM model to detect sarcasmSarcasm in Hindi text. Both models perform satisfactorily in terms of precision, recall, F-score, and accuracy, although theHindiBERT HindiBERT-LSTM (Long Term-Short Memory) model achieves higher performance overall. This model’s accuracy is 96% on average.