In recent years, the growing use of Internet and Social media has produced a huge amount and variety of data, commonly known as Big Data. They consist of traces that everyone leaves online, and that can be exploited to investigate human behaviour, preferences and habits. This kind of information represents a fruitful resource in different fields, from Education to Marketing, from Management to Biology or Computer Science. This chapter aims at exploring students’ perception of three famous Universities using data collected by social networks. In particular, perception regards services, social and instructional characteristics, and data are gathered from Instagram, a popular social network, analyzed using machine learning techniques (sentiment analysis and emotion recognition). Results show a high satisfaction level regarding the investigated features.

The predictive power of Social Media through Content Analytics to investigate the perception of University reality

Simona Giglio;Lorella Gabriele;Francesca Bertacchini
2020

Abstract

In recent years, the growing use of Internet and Social media has produced a huge amount and variety of data, commonly known as Big Data. They consist of traces that everyone leaves online, and that can be exploited to investigate human behaviour, preferences and habits. This kind of information represents a fruitful resource in different fields, from Education to Marketing, from Management to Biology or Computer Science. This chapter aims at exploring students’ perception of three famous Universities using data collected by social networks. In particular, perception regards services, social and instructional characteristics, and data are gathered from Instagram, a popular social network, analyzed using machine learning techniques (sentiment analysis and emotion recognition). Results show a high satisfaction level regarding the investigated features.
978-1-53618-040-4
content analytics and perception, Big Data and human behaviour, sentiment analysis, emotion recognition, student’s behaviour analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/309979
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