Hybrid Quantum-Classical Machine Learning for Sentiment Analysis

Autor:	Masum, Abu Kaisar Mohammad, Maurya, Anshul, Murthy, Dhruthi Sridhar, Pratibha, Mahmud, Naveed
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 3 figures, To Appear at IEEE ICMLA 2023, FL, USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.10672 Zobrazit plný text záznamu View this record from Arxiv