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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Abu Kaisar Mohammad Masum, Maurya, Anshul, Dhruthi Sridhar Murthy, Pratibha, Mahmud, Naveed
Format: Article
Language:English
Subjects:
Algorithms
Circuits
Data mining
Datasets
Machine learning
Natural language processing
Quantum computing
Reduction
Sentiment analysis
Wavelet transforms
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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:2331-8422