Hybrid Quantum-Classical Machine Learning for Sentiment Analysis
Abu Kaisar Mohammad Masum Anshul Maurya Dhruthi Sridhar Murthy Pratibha Naveed Mahmud
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.