  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.
