Hybrid Quantum-Classical Machine Learning for Sentiment Analysis

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 analysi...

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Bibliographic Details
Main Authors: Mohammad Masum, Abu Kaisar, Maurya, Anshul, Murthy, Dhruthi Sridhar, Pratibha, Mahmud, Naveed
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Collaboration
Dimensionality reduction
Haar Transform
Machine learning algorithms
Quantum computing
Quantum Machine Learning
Sentiment analysis
SVM
Wavelet transforms
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Summary: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.
ISSN:1946-0759
DOI:10.1109/ICMLA58977.2023.00149