Qubits and Sentiments: Unveiling New Perspectives in Hindi Textual Data
Keywords:
Quantum Neural Network (QNN), Quantum Variational Autoencoder (Q-VAE), Tuned Quantum CNN, Sequential-based hyperband optimization technique, Hindi movie reviews.Abstract
Sentiment analysis (SA) is a critical component of Natural Language Processing (NLP), particularly for automatic text classification. However, previous approaches have shortcomings in capturing nuances such as negation, word pairings, and contextual understanding, particularly in languages like Hindi. To solve these issues, this research offers a novel Quantum Neural Network (QNN) technique designed specifically for sentiment analysis in Hindi text data, which employs quantum computing concepts to capture linguistic nuances and context better. This study uses a Quantum Variational Auto Encoder to encode classical data into a quantum form, capturing diverse sentiments like sarcasm and colloquial expressions. A Tuned Quantum Convolutional Neural Network architecture is introduced to capture complex linguistic syntax. A novel Sequential-based hyperband optimization technique is used to enhance model performance. The hybrid approach significantly improves accuracy and efficiency in handling quantum data, contributing to the advancement of SA, particularly in Hindi Movie Reviews. The findings demonstrate that the proposed strategy performs best with accuracy 97.64 %, precision 85.93%, recall 99.17%, F-1 score 92.20%, than other accepted strategies.
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