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Sentiment analysis is a branch of Natural Language Processing (NLP) whichgoal is to assign sentiments or emotions to particular sentences or words.Performing this task is particularly useful for companies wishing to take intoaccount customer feedback through chatbots or verbatim. This has been doneextensively in the literature using various approaches, ranging from simplemodels to deep transformer neural networks. In this paper, we will tacklesentiment analysis in the Noisy Intermediate Scale Computing (NISQ) era, usingthe DisCoCat model of language. We will first present the basics of quantumcomputing and the DisCoCat model. This will enable us to define a generalframework to perform NLP tasks on a quantum computer. We will then extend thetwo-class classification that was performed by Lorenz et al. (2021) to afour-class sentiment analysis experiment on a much larger dataset, showing thescalability of such a framework.

A multiclass Q-NLP sentiment analysis experiment using DisCoCat