Hybrid Quantum-Classical Machine Learning for Sentiment Analysis
This work addresses sentiment analysis for natural language processing applications, but it appears incremental as it combines existing quantum and classical techniques without a major breakthrough.
The authors tackled sentiment analysis by proposing a hybrid quantum-classical machine learning approach, integrating quantum kernel methods and variational circuits with classical dimension reduction techniques like PCA and Haar wavelet transform, and found that it performed consistently better than classical methods on English and Bengali datasets.
The collaboration between quantum computing and classical machine learning offers potential advantages in natural language processing, particularly in the sentiment analysis of human emotions and opinions expressed in large-scale datasets. In this work, we propose a methodology for sentiment analysis using hybrid quantum-classical machine learning algorithms. We investigate quantum kernel approaches and variational quantum circuit-based classifiers and integrate them with classical dimension reduction techniques such as PCA and Haar wavelet transform. The proposed methodology is evaluated using two distinct datasets, based on English and Bengali languages. Experimental results show that after dimensionality reduction of the data, performance of the quantum-based hybrid algorithms were consistent and better than classical methods.