Quantum-Inspired Model Based on Convolutional Neural Network for Sentiment Analysis
Si Li, Yuexian Hou · 2021
Sentiment analysis aims to judge the sentiment polarity of various types of text at the document and sentence level. It has important theoretical and practical significance and is a hot topic in natural language processing. Although existing sentiment analysis methods based on sentiment dictionaries and machine learning consider contextual semantic information, they still have the problem of not being able to effectively encode the mixing of semantic subspaces, and therefore ignore the feature interaction between sentiment words. To solve this problem, this article combines quantum mechanics, deep learning and natural language processing technology, and introduces the concept of density matrix into the convolutional neural network. The density matrix can encode more semantic dependencies and can be integrated into the neural network architecture. We proposed a Quantum-inspired Model based on Convolutional Neural Network for Sentiment Analysis (QI-CNN). Experiments on IMDB English dataset and Weibo Chinese dataset verify the effectiveness of our proposed model.