Quantum Fuzzy Neural Network for multimodal sentiment and sarcasm detection

Sentiment and sarcasm detection in social media contribute to assessing social opinion trends. Over years, most artificial intelligence (AI) methods have relied on real values to characterize the sentimental sarcastic features in language. These methods often overlook the complexity and uncertainty of sentimental and sarcastic elements in human language. Therefore, this paper proposes the Quantum Fuzzy Neural Network (QFNN), a multimodal fusion and multitask learning algorithm with a Seq2Seq structure that combines Classical and Quantum Neural Networks (QNN), and fuzzy logic. Complex numbers are used in the Fuzzifier to capture sentiment and sarcasm features, and QNN are used in the Defuzzifier to obtain the prediction. The experiments are conducted on classical computers by constructing quantum circuits in a simulated environment. The results show that QFNN can outperform several recent methods in sarcasm and sentiment detection task on two datasets (Mustard and Memotion). Moreover, by assessing the fidelity of quantum circuits in a noisy environment, QFNN was found to have excellent robustness. The QFNN circuit also possesses expressible and entanglement capabilities, proving effective in various settings. Our code is available https://github.com/prayagtiwari/QFNN.

Automated summary

This paper proposes a Quantum Fuzzy Neural Network (QFNN) for sentiment and sarcasm detection in social media. The QFNN combines Classical and Quantum Neural Networks (QNN) with fuzzy logic and utilizes complex numbers to capture sentimental and sarcastic features. The experiments show that QFNN outperforms other methods and exhibits excellent robustness and expressibility.