A multiclass Q-NLP sentiment analysis experiment using DisCoCat

TL;DR

This paper explores quantum computing for sentiment analysis using the DisCoCat model, extending previous two-class classification to a scalable four-class experiment on larger datasets.

Contribution

It introduces a quantum NLP framework based on DisCoCat and demonstrates its scalability for multi-class sentiment analysis in the NISQ era.

Findings

Successful extension from two-class to four-class sentiment classification

Demonstrated scalability of quantum NLP framework on larger datasets

Provided a general quantum NLP framework applicable to various tasks

Abstract

Sentiment analysis is a branch of Natural Language Processing (NLP) which goal is to assign sentiments or emotions to particular sentences or words. Performing this task is particularly useful for companies wishing to take into account customer feedback through chatbots or verbatim. This has been done extensively in the literature using various approaches, ranging from simple models to deep transformer neural networks. In this paper, we will tackle sentiment analysis in the Noisy Intermediate Scale Computing (NISQ) era, using the DisCoCat model of language. We will first present the basics of quantum computing and the DisCoCat model. This will enable us to define a general framework to perform NLP tasks on a quantum computer. We will then extend the two-class classification that was performed by Lorenz et al. (2021) to a four-class sentiment analysis experiment on a much larger dataset, showing the scalability of such a framework.