Quantum Assisted Superior Sentiment Classification for Optimized Review

Understanding people's emotions is becoming increasingly important in diverse areas such as social media, e-commerce, and brand or trend analysis. While both deep learning networks and traditional machine learning techniques, such as Support Vector Machines (SVM) have their uses, they often fail to efficiently handle very big or complicated datasets. The study of Quantum Machine Learning (QML) methods could help solve these problems. Support Vector Machine (SVM) quantum algorithms are a scalable way to work with large amounts of data. They are more accurate at classifying datasets than traditional SVM while using less computing power. We suggest a mixed model that blends QSVM and CNN for deep learning and quantum processing that work together. The method includes extracting features, classifying mood, and preparing data from two standard sets: Amazon reviews of goods and IMDb reviews of movies. As shown in experiments on a large dataset, the suggested QMLbased hybrid design method is up to 12 % more accurate and nearly 20 % faster when working than standard machine learning methods. The new development makes quantum computing useful for faster and more flexible data processing to help with mood classification in areas like brand intelligence, watching social media, and reviewing customer reviews.