Paolo Lindia


Eugenio Cesario


Paolo Trunfio

Research Topic

Big Data analysis, machine learning, sentiment analysis, opinion mining, NLP

Research Abstract

The PhD project proposal aims to study innovative machine learning and data mining techniques for analysing large volumes of data.

For this purpose, methodologies will be developed for the collection of data from different sources (social media, open data, web data, public portals, etc.), and new algorithms will be designed and implemented for the extraction of descriptive/predictive knowledge models useful for decision makers to make the most appropriate and effective decisions for a given task.

One potential application domain is corporate marketing supported by urban data. In particular, the idea is to propose data analysis techniques that, by analysing corporate marketing data (sales, user interests, etc.) and urban data (mobility, security, geo-referenced events, services, schooling, etc.), can estimate the sentiment and polarisation of users expressed with respect to products, and on the basis of this information, suggest effective marketing strategies. Such models, based on an integration of corporate and urban data, can be useful to both corporate Marketing Advisors and City Managers of a Smart City