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IEEE International Symposium on Consumer Electronics (ISCE)

Comparative Study of Traditional Machine Learning and Quantum Computing in Natural Language Processing: A Case Study on Sentiment Analysis

Yang Li, Thatsanee Charoenporn, Virach Sornlertlamvanich

Published Aug 13, 2024• 1 citation
Publisher

Abstract

This research paper primarily investigates the application and performance of traditional machine learning and quantum computing in Natural Language Processing (NLP), with a focus on sentiment analysis tasks. By comparing the accuracy, efficiency, and scalability of these two technologies, the study aims to reveal the potential of quantum computing in handling complex NLP tasks and to provide data support for future technology choices and research directions. The paper also details the use of 1M DB and NLTK movie review datasets for experiments and discusses the experimental design, performance evaluation results, and technical challenges faced by both methods.

DOI
10.1109/isct62336.2024.10791272

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This research paper primarily investigates the application and performance of traditional machine learning and quantum computing in Natural Language Processing (NLP), with a focus on sentiment analysis tasks. By comparing the accuracy, efficiency, and scalability of these two technologies, the study aims to reveal the potential of quantum computing in handling complex NLP tasks and to provide data support for future technology choices and research directions. The paper also details the use of 1M DB and NLTK movie review datasets for experiments and discusses the experimental design, performance evaluation results, and technical challenges faced by both methods.
Emotion Recognition plays an important role in recognizing human behavior. Human behavior may be represented in text, speech, and facial expressions. In traditional form, natural language processing known as NLP is used to extract features to evaluate emotions. With the evolution of Quantum Computing, Quantum Machine learning often called QML is one of the research areas used to evaluate emotions. This paper is designed to compare traditional ML models with QML models to analyze the sentiments and emotions of multimodal data. Here, NLP techniques are used for preprocessing. A hybrid model is built by combining NLP and QML. The preprocessed data are transmitted into quantum circuits for computation, and its results are compared with conventional machine learning models. This evaluation identifies QML as a promising future as it is useful for large and complex data sets with high dimensions. This study highlights the potential of QML in NLP and outlines the challenges such as encoding and hardware limitations.
Quantum computers has the capacity to enhance the speed of intricate calculations, achieve parallelism via superpositions, and effectively process vast amounts of data. In addition, the domain of natural language processing (NLP) is swiftly drawing the attention of academics and engineers that want to construct more extensive computational models for NLP. Hence, the use of quantum technology in NLP endeavours, particularly in sentiment categorization, has promising prospects for further advancement. In this research work, a detailed analysis of different sentiment classification methods are investigated that are interpreted with quantum computing models. To be precise the Quantum Long Short Term Memory (QLSTM) and Quantum Support Vector Machines (QSVM) are examined to determine the efficiency among the models. The results are simulated in Qiskit simulator and real time quantum computer Qiskit_ibm_osaka to evaluate the examined models. The results shows the significance of the experimentation and it is proven using Accuracy, precision and recall as performance metrices. On an average, in terms of accuracy the proposed model outperforms existing methods with 8.9%, On precision the proposed model outperforms 11.1% compared to existing methods. And with respect to recall the proposed model outperforms 6.7% compared to existing methods.
Quantum computing with its capacity to compute multiple possibilities at once, and perform some kinds of calculations almost instantly is one leading edge. The integration of QCs and DL undoubtedly represent one such transformative means for improving Natural Language Processing (NLP). By combining the two, this uses quantum principles of superposition and entanglement to make computation faster regarding human language comprehension limitations in classical computing. Deep learning models, specifically neural networks have already made significant improvements over NLP tasks like language translations, sentiment analysis and text generation. Nevertheless, these models also face scalability and computational efficiency issues with huge volumes of data. Quantum computing offers a solution to all this by reducing the time exponentially that it takes to fetch large amounts of language data and running deep learning algorithms. This paper investigates the opportunities that integrating quantum computing and deep learning into NLP practices might unlock. We drafted the fundamentals of quantum computing and why it is a need over classical computing, where we discussed on how quantum beneficial for data processing & optimization of an algorithm. We then explore applications of quantum enhanced deep learning for NLP, with a focus on the impact that training and inference can have on model performance. We demonstrate the utility of this integration by integrating our four SQDs to showcase improvements on diverse NLP tasks through a series of experiments and case studies. We also demonstrate that hybrid quantum-classical approaches allow the combination of traditional ML and quantum-enhanced models, achieving state-of-the-art performance with respect to effectiveness, scalability and precision
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This research introduces a new paradigm for sentiment analysis on patient medication reviews through the application of quantum computing to improve machine learning models. A pre-existing dataset of medication reviews from the UCI Machine Learning repository was utilized, involving Quantum Natural Language Processing (QNLP) techniques, namely the application of quantum embeddings produced by quantum circuits to improve the performance of sentiment classification. Experimental findings indicate that improved models through quantum techniques, like the Quantum Random Forest, outperform conventional models in terms of accuracy and precision. These improvements are ascribed to quantum embeddings’ capacity to efficiently represent complex linguistic patterns in high-dimensional data. Additionally, the study uses robust validation techniques including performance benchmarking and cross-validation to guarantee reliability. Through a scalable and context-aware framework for natural language comprehension, this study highlights the revolutionary potential of QNLP in improving sentiment analysis in the healthcare industry.
Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.