In this paper the interactions between television audience and social networks have been analysed, especially considering Twitter data. In this experiment about 2 and a half million tweets were collected, for 14 USA TV series in a nine weeks period through the use of an ad-hoc crawler created for this purpose. Subsequently, tweets were classified according to their sentiment (positive, negative, neutral) using an original method based on the use of decision trees. A linear regression model was then used to analyse the data.

Forecasting with Twitter data: an application to USA TV series audience

Molteni, Luca;Ponce De Leon, Joaquin
2016

Abstract

In this paper the interactions between television audience and social networks have been analysed, especially considering Twitter data. In this experiment about 2 and a half million tweets were collected, for 14 USA TV series in a nine weeks period through the use of an ad-hoc crawler created for this purpose. Subsequently, tweets were classified according to their sentiment (positive, negative, neutral) using an original method based on the use of decision trees. A linear regression model was then used to analyse the data.
2016
2016
Molteni, Luca; Ponce De Leon, Joaquin
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/3989772
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