Attention-based Hybrid Neural Network Model for Quantum-Enhanced Sentiment Analysis

Abstract

Sentiment analysis is considered as one of the basic tasks in natural language processing (NLP) has been undergoing incredible developments with the introduction of deep learning structures. Nevertheless, traditional neural network methods are limited by major restrictions when it comes to modeling of long-range semantic interactions, contextual ambiguities, and multi-granular linguistic characteristics of affective text. In this paper, we introduce an innovative Attention based Hybrid Neural Network (ABHNN) with quantum enhanced feature representations to provide high-precision sentiment classification in a variety of textual environments. The given architecture is a synthesis of bidirectional Long Short-Term Memory (BiLSTM) networks, Transformer-based multi-head self-attention, Convolutional Neural Networks (CNNs) and quantum-inspired feature encoding layers into one end-to-end trainable pipeline. The ABHNN model proposes three main novelties, namely (i) a Quantum-Enhanced Embedding Layer (QEEL), which uses variational quantum circuits to encode the semantic features in quantum probability amplitudes; (ii) a Hierarchical Multi-Head Cross-Attention (HMCA) module, which runs simultaneously on word, phrase, sentence, levels; and (iii) a Gated Hybrid Fusion Network (GHFN) which dynamically chooses between the contribution of local convolutional feature and global recurrent-attentive feature. Massive experiments on SST-2, IMDB, SemEval-2017 Task 4, and Yelp Polarity show that the accuracy improvement is between 2.1 and 4.7% above the state-of-the-art baselines. The model has also been shown to be more robust in noisy scenarios, the domain shift, and the low-resource scenario.