Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment Analysis

TL;DR

This paper introduces a quantum-inspired deep learning architecture for text sentiment analysis that enhances accuracy, efficiency, and interpretability by integrating quantum mechanics principles with advanced neural network techniques.

Contribution

It presents a novel quantum-inspired text representation and embedding method, combined with LSTM and self-attention, to improve sentiment analysis interpretability and performance.

Findings

Significant accuracy improvements over previous models

Enhanced interpretability through quantum principles

Efficient feature extraction with LSTM, SAMs, and CNNs

Abstract

Text has become the predominant form of communication on social media, embedding a wealth of emotional nuances. Consequently, the extraction of emotional information from text is of paramount importance. Despite previous research making some progress, existing text sentiment analysis models still face challenges in integrating diverse semantic information and lack interpretability. To address these issues, we propose a quantum-inspired deep learning architecture that combines fundamental principles of quantum mechanics (QM principles) with deep learning models for text sentiment analysis. Specifically, we analyze the commonalities between text representation and QM principles to design a quantum-inspired text representation method and further develop a quantum-inspired text embedding layer. Additionally, we design a feature extraction layer based on long short-term memory (LSTM) networks and self-attention mechanisms (SAMs). Finally, we calculate the text density matrix using the quantum complex numbers principle and apply 2D-convolution neural networks (CNNs) for feature condensation and dimensionality reduction. Through a series of visualization, comparative, and ablation experiments, we demonstrate that our model not only shows significant advantages in accuracy and efficiency compared to previous related models but also achieves a certain level of interpretability by integrating QM principles. Our code is available at QISA.