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2015 | OriginalPaper | Chapter

Sentiment Analysis for Government: An Optimized Approach

Authors : Angelo Corallo, Laura Fortunato, Marco Matera, Marco Alessi, Alessio Camillò, Valentina Chetta, Enza Giangreco, Davide Storelli

Published in: Machine Learning and Data Mining in Pattern Recognition

Publisher: Springer International Publishing

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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”.

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previous chapter SentiSAIL: Sentiment Analysis in English, German and Russian
next chapter A Novel Algorithm for the Integration of the Imputation of Missing Values and Clustering
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Metadata
Title
Sentiment Analysis for Government: An Optimized Approach
Authors
Angelo Corallo
Laura Fortunato
Marco Matera
Marco Alessi
Alessio Camillò
Valentina Chetta
Enza Giangreco
Davide Storelli
Copyright Year
2015
Book
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_7

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