This paper describes a Sentiment Analysis (SA) method to analyze tweets polarity and to enable government to describe quantitatively the opinion of active users on social networks with respect to the topics of interest to the Public Administration. We propose an optimized approach employing a document-level and a dataset-level supervised machine learning classifier to provide accurate results in both individual and aggregated sentiment classification. The aim of this work is also to identify the types of features that allow to obtain the most accurate sentiment classification for a dataset of Italian tweets in the context of a Public Administration event, also taking into account the size of the training set. This work uses a dataset of 1,700 Italian tweets relating to the public event of “Lecce 2019 – European Capital of Culture”.
Sentiment Analysis for Government: An Optimized Approach
CORALLO, Angelo;FORTUNATO, LAURA;CAMILLO', ALESSIO;CHETTA, VALENTINA;GIANGRECO, ENZA;STORELLI, DAVIDE SERGIO
2015-01-01
Abstract
This paper describes a Sentiment Analysis (SA) method to analyze tweets polarity and to enable government to describe quantitatively the opinion of active users on social networks with respect to the topics of interest to the Public Administration. We propose an optimized approach employing a document-level and a dataset-level supervised machine learning classifier to provide accurate results in both individual and aggregated sentiment classification. The aim of this work is also to identify the types of features that allow to obtain the most accurate sentiment classification for a dataset of Italian tweets in the context of a Public Administration event, also taking into account the size of the training set. This work uses a dataset of 1,700 Italian tweets relating to the public event of “Lecce 2019 – European Capital of Culture”.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
