  Social Media and Internet have the potential to be exploited as a source of
opinion to enrich Decision Making solutions. Crowd Decision Making (CDM) is a
methodology able to infer opinions and decisions from plain texts, such as
reviews published in social media platforms, by means of Sentiment Analysis.
Currently, the emergence and potential of Large Language Models (LLMs) lead us
to explore new scenarios of automatically understand written texts, also known
as natural language processing. This paper analyzes the use of ChatGPT based on
prompt design strategies to assist in CDM processes to extract opinions and
make decisions. We integrate ChatGPT in CDM processes as a flexible tool that
infer the opinions expressed in texts, providing numerical or linguistic
evaluations where the decision making models are based on the prompt design
strategies. We include a multi-criteria decision making scenario with a
category ontology for criteria. We also consider ChatGPT as an end-to-end CDM
model able to provide a general opinion and score on the alternatives. We
conduct empirical experiments on real data extracted from TripAdvisor, the
TripR-2020Large dataset. The analysis of results show a promising branch for
developing quality decision making models using ChatGPT. Finally, we discuss
the challenges of consistency, sensitivity and explainability associated to the
use of LLMs in CDM processes, raising open questions for future studies.
