Emotion Classification for Hindi Text: A Hybrid Approach
Dhanashree S. Kulkarni, Anand Deshpande, Vania Vieira Estrela · ACM Transactions on Asian and Low-Resource Language Information Processing · 2025
Emotion classification is a critical component in understanding human expression and interaction in textual data. However, achieving high accuracy in emotion classification, particularly in complex languages like Hindi, presents several challenges. This article proposes a hybrid method for emotion classification in Hindi text, combining a knowledge-based approach for stability and a statistical approach for accuracy. Machine learning (ML) techniques, while powerful, require extensive training data and may not generalize well across different domains. Conversely, lexicon-based methods offer consistent performance across domains but often lack precision. To leverage the advantages of both methods, a hybrid model for emotion analysis in Hindi is presented. Results indicate that the hybrid model outperforms both individual classifiers and the simple lexicon approach in terms of accuracy. Specifically, incorporating Multinomial Naïve Bayes within the hybrid framework yields significant performance improvements. The proposed hybrid model presents a viable and superior approach for Hindi emotion analysis, balancing the need for stability and accuracy across varied textual domains