Quantum Representation for Sentiment Classification

Ruskanda, Fariska Zakhralativa; Abiwardani, Muhammad Rifat; Bari, Muhammad Akram Al; Bagaspati, Kinantan Arya; Mulyawan, Rahmat; Larasati, Harashta Tatimma

doi:10.1109/qce53715.2022.00025

Cited by 4 publications

(18 citation statements)

References 18 publications

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“…DisCoCat is used as the language representation of the QNLP pipeline in [4,6]. DisCoCat has been used for question-answering [22], language translation [23], relative pronoun detection [24], and sentiment classification [9][10][11].…”

Section: B Language Representation For Quantum Natural Language Proce...mentioning

confidence: 99%

“…In addition to seminal papers that establish the fundamental theories, other researchers have applied QNLP theory to real-life use cases within natural language processing. Among them is [9][10][11], which uses the QNLP framework previously described in order to perform sentiment analysis. Sentiment analysis is a task within the field of NLP that aims to analyze the opinions or sentiments of a speaker through the use of sentences in natural language [12].…”

Section: Introductionmentioning

confidence: 99%

“…That is, binary sentiment classification allows the output register to be represented by a single qubit; positive or negative sentiment is assigned to the computational basis states |0⟩ and |1⟩. Such is also done in [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…In [9], sentiment classification is performed by measuring a sentence's PQC bit string output and interpreting it as the model's prediction. The paper integrates the DisCoCat model to represent sentences in string diagrams and uses IQP ansatz to transcribe the diagrams into PQCs.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this research aims to find an alternative hybrid quantum-classical machine learning method for sentiment analysis with better performance. In particular, this research is intended as a direct comparison of performance to the model offered in [9]. Specifically, this research implements an alternative ansatz that is better tailored for sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

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Simple Sentiment Analysis Ansatz for Sentiment Classification in Quantum Natural Language Processing

Ruskanda,

Abiwardani,

Syafalni

et al. 2023

IEEE Access

Self Cite

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Sentiment classification is a valuable application of natural language processing that has seen wide usage in optimizing business processes. This paper explores a novel implementation of sentiment analysis using the Variational Quantum Algorithms (VQA) framework. As ansatz choice determines model performance in VQA, this paper proposes an alternative ansatz for the sentiment classification task in quantum representation. Specifically, it builds upon previous work in quantum sentiment classification by proposing an alternative ansatz to the Instantaneous Quantum Polytime ansatz, entitled Simple Sentiment Analysis (SimpleSA) ansatz. A key feature of the SimpleSA ansatz is the decision to neglect noun parameterization. The proposed SimpleSA has less complexity than the other ansätze in terms of number of parameters and number of gates. Moreover, experimental results show that the SimpleSA ansatz with H-CNOT-Rz-H compound block construction outperforms the Instantaneous Quantum Polytime (IQP) ansatz at 85.00% accuracy. Furthermore, SimpleSA optimization converges 20.89% faster than Instantaneous Quantum Polytime (IQP) for the Simultaneous Perturbation Stochastic Approximation (SPSA) method with 130 iterations. The proposed work is useful for applications of quantum computers for sentiment analyses and classifications.INDEX TERMS quantum natural language processing, sentiment classification, ansatz

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Section: B Language Representation For Quantum Natural Language Proce...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simple Sentiment Analysis Ansatz for Sentiment Classification in Quantum Natural Language Processing

Ruskanda,

Abiwardani,

Syafalni

et al. 2023

IEEE Access

Self Cite

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Near-term advances in quantum natural language processing

Widdows,

Alexander,

Zhu

et al. 2024

Ann Math Artif Intell

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Quantum computing and machine learning for Arabic language sentiment classification in social media

Omar,

Abd El-Hafeez

2023

Sci Rep

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With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data.

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Quantum Representation for Sentiment Classification

Cited by 4 publications

References 18 publications

Simple Sentiment Analysis Ansatz for Sentiment Classification in Quantum Natural Language Processing

Simple Sentiment Analysis Ansatz for Sentiment Classification in Quantum Natural Language Processing

Near-term advances in quantum natural language processing

Quantum computing and machine learning for Arabic language sentiment classification in social media

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