In recent years, Sentiment Analysis has become one of the most interesting topics in AI research due to its promising commercial benefits. An important step in a Sentiment Analysis system for text mining is the preprocessing phase, but it is often underestimated and not extensively covered in literature. In this work, our aim is to highlight the importance of preprocessing techniques and show how they can improve system accuracy. In particular, some different preprocessing methods are presented and the accuracy of each of them is compared with the others. The purpose of this comparison is to evaluate which techniques are effective. In this paper, we also present the reasons why the accuracy improves, by means of a precise analysis of each method.

A comparison between preprocessing techniques for sentiment analysis in Twitter / Angiani, G.; Ferrari, L.; Fontanini, T.; Fornacciari, P.; Iotti, E.; Magliani, F.; Manicardi, S.. - 1748:(2016). ((Intervento presentato al convegno 2nd International Workshop on Knowledge Discovery on the WEB, KDWEB 2016 tenutosi a ita nel 2016.

A comparison between preprocessing techniques for sentiment analysis in Twitter

Angiani G.;Fontanini T.;Fornacciari P.;Iotti E.;Magliani F.;
2016

Abstract

In recent years, Sentiment Analysis has become one of the most interesting topics in AI research due to its promising commercial benefits. An important step in a Sentiment Analysis system for text mining is the preprocessing phase, but it is often underestimated and not extensively covered in literature. In this work, our aim is to highlight the importance of preprocessing techniques and show how they can improve system accuracy. In particular, some different preprocessing methods are presented and the accuracy of each of them is compared with the others. The purpose of this comparison is to evaluate which techniques are effective. In this paper, we also present the reasons why the accuracy improves, by means of a precise analysis of each method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2872050
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