Sentiment analysis is a branch of Natural Language Processing (NLP) which goal is to assign sentiments or emotions to particular sentences or words. Performing this task is particularly useful for companies wishing to take into account customer feedback through chatbots or verbatim. This has been done extensively in the literature using various approaches, ranging from simple models to deep transformer neural networks. In this paper, we will tackle sentiment analysis in the Noisy Intermediate Scale Computing (NISQ) era, using the DisCoCat model of language. We will first present the basics of quantum computing and the DisCoCat model. This will enable us to define a general framework to perform NLP tasks on a quantum computer. We will then extend the two-class classification that was performed by Lorenz et al. (2021) to a four-class sentiment analysis experiment on a much larger dataset, showing the scalability of such a framework.
Sentiment analysis is a branch of Natural Language Processing (NLP) which goal is to assign sentiments or emotions to particular sentences or words. Performing this task is particularly useful for companies wishing to take into account customer feedback through chatbots or verbatim. This has been done extensively in the literature using various approaches, ranging from simple models to deep transformer neural networks. In this paper, we will tackle sentiment analysis in the Noisy Intermediate Scale Computing (NISQ) era, using the DisCoCat model of language. We will first present the basics of quantum computing and the DisCoCat model. This will enable us to define a general framework to perform NLP tasks on a quantum computer. We will then extend the two-class classification that was performed by Lorenz et al. (2021) to a four-class sentiment analysis experiment on a much larger dataset, showing the scalability of such a framework.
Sentiment analysis (SA) is an essential component of natural language processing (NLP) and is used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning (ML) and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in SA. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. In addition, they are frequently insensitive to input variations. In this article, we propose a novel hybrid approach for SA called the quantum fuzzy neural network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical SA algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results show that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with SA on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyzes different forms of textual data, thus improving sentiment classification and insights associated with SA.
As part of the recent research effort on quantum natural language processing (QNLP), variational quantum sentence classifiers (VQSCs) have been implemented and supported in lambeq / DisCoPy, based on the DisCoCat model of sentence meaning. We discuss in some detail VQSCs, including category theory, DisCoCat for modeling sentence as string diagram, and DisCoPy for encoding string diagram as parameterized quantum circuit. Many NLP tasks, however, require the handling of text consisting of multiple sentences, which is not supported in lambeq / DisCoPy. A good example is sentiment classification of customer feedback or product review. We discuss three potential approaches to variational quantum text classifiers (VQTCs), in line with VQSCs. The first is a weighted bag-of-sentences approach which treats text as a group of independent sentences with task-specific sentence weighting. The second is a coreference resolution approach which treats text as a consolidation of its member sentences with coreferences among them resolved. Both approaches are based on the DisCoCat model and should be implementable in lambeq / DisCoCat. The third approach, on the other hand, is based on the DisCoCirc model which considers both ordering of sentences and interaction of words in composing text meaning from word and sentence meanings. DisCoCirc makes fundamental modification of DisCoCat since a sentence in DisCoCirc updates meanings of words, whereas all meanings are static in DisCoCat. It is not clear if DisCoCirc can be implemented in lambeq / DisCoCat without breaking DisCoCat.
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.
Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh, and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NLP models that solve simple sentence classification tasks on quantum hardware. We conduct quantum simulations that compare the syntax-sensitive model of Coecke et al. with two baselines that use less or no syntax; specifically, we implement the quantum analogues of a “bag-of-words” model, where syntax is not taken into account at all, and of a word-sequence model, where only word order is respected. We demonstrate that all models converge smoothly both in simulations and when run on quantum hardware, and that the results are the expected ones based on the nature of the tasks and the datasets used. Another important goal of this paper is to describe in a way accessible to AI and NLP researchers the main principles, process and challenges of experiments on quantum hardware. Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.
This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.
The quantum computing made complex computations may be executed at light speed, superpositions allow for parallelism, and massive quantities of data can be efficiently processed. Furthermore, researchers and engineers are quickly becoming interested in natural language processing (NLP) as a topic for developing more comprehensive computational models of NLP. Therefore, there is hope for future progress in NLP efforts that use quantum technology, especially in emotion classification. In this study, we use quantum computing models to delve deeply into two sentiment categorization approaches. To be more specific, the effectiveness of the models is evaluated by looking at VQC and QSVM. The analyzed models are evaluated by simulations in the Qiskit simulator and the real-time quantum computer Qiskit_ibm_osaka. Results demonstrate the importance of the experiment and validate it using positive predicted value, sensitivity and Fl-Score as performance metrices.