  With the increase of digital data and social network platforms the impact of
social media science in driving company decision related to product/service
features and customer care operations is becoming more crucial. In particular,
platform such as Twitter where people can share experience about almost
everything can drastically impact the reputation and offering of a company as
well as of a place or tourism site. Text mining tools are researched and
proposed in literature in order to gain value and perform trend topics and
sentiment analysis on Twitter. As data are the fuels for these models, the
"right" ones, i.e the domain-related ones makes a difference on their accuracy.
In this paper, we describe a pipeline of \textit{DataOps / MLOps} operations
performed over a tourism related Twitter dataset in order to comprehend tourism
motivation and interest. The gained knowledge can be exploit, by the
travel/hospitality industry in order to develop data-driven strategic service,
and by travelers which can consume relevant information about tourist
destination.
