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<p>Emotion detection in code-mixed text presents unique challenges due to the blending of linguistic structures and informal usage patterns. Existing methods often struggle to effectively capture semantic and contextual nuances in such data. This paper proposes an optimized hybrid deep learning model for emotion classification in code-mixed Hinglish text. The framework integrates mBERT-based token embeddings with a CNN-BiLSTM architecture enhanced by attention mechanisms to effectively capture semantic and contextual features. Evaluated on Task 9 of the SemEval-2020 dataset, the model achieves an accuracy of 93.41%, precision of 91.46%, recall of 91.45%, and F1-score of 91.44. Experimental results demonstrate the effectiveness of the proposed architecture in handling the linguistic complexity of multilingual and mixed-language environments.</p>

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Optimized emotion classification in code-mixed Hinglish text using an mBERT based hybrid neural network with attention mechanisms

  • 期刊论文

  • 发表日期：

    2025年11月17日

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  • Brajesh Kumar Khare,
  • Imran Khan

摘要

Emotion detection in code-mixed text presents unique challenges due to the blending of linguistic structures and informal usage patterns. Existing methods often struggle to effectively capture semantic and contextual nuances in such data. This paper proposes an optimized hybrid deep learning model for emotion classification in code-mixed Hinglish text. The framework integrates mBERT-based token embeddings with a CNN-BiLSTM architecture enhanced by attention mechanisms to effectively capture semantic and contextual features. Evaluated on Task 9 of the SemEval-2020 dataset, the model achieves an accuracy of 93.41%, precision of 91.46%, recall of 91.45%, and F1-score of 91.44. Experimental results demonstrate the effectiveness of the proposed architecture in handling the linguistic complexity of multilingual and mixed-language environments.