  Twitter is among the most used online platforms for the political
communications, due to the concision of its messages (which is particularly
suitable for political slogans) and the quick diffusion of messages. Especially
when the argument stimulate the emotionality of users, the content on Twitter
is shared with extreme speed and thus studying the tweet sentiment if of utmost
importance to predict the evolution of the discussions and the register of the
relative narratives. In this article, we present a model able to reproduce the
dynamics of the sentiments of tweets related to specific topics and periods and
to provide a prediction of the sentiment of the future posts based on the
observed past. The model is a recent variant of the P\'olya urn, introduced and
studied in arXiv:1906.10951 and arXiv:2010.06373, which is characterized by a
"local" reinforcement, i.e. a reinforcement mechanism mainly based on the most
recent observations, and by a random persistent fluctuation of the predictive
mean. In particular, this latter feature is capable of capturing the trend
fluctuations in the sentiment curve. While the proposed model is extremely
general and may be also employed in other contexts, it has been tested on
several Twitter data sets and demonstrated greater performances compared to the
standard P\'olya urn model. Moreover, the different performances on different
data sets highlight different emotional sensitivities respect to a public
event.
