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EVALITA. Evaluation of NLP and Speech Tools for Italian

 | 
Pierpaolo Basile
, 
Franco Cutugno
, 
Malvina Nissim
, 
et al.

Part II: EVALITA 2016: Task overviews and participants reports

Exploiting Emotive Features for the Sentiment Polarity Classification of tweets

Lucia C. Passaro, Alessandro Bondielli et Alessandro Lenci

Résumé

This paper describes the CoLing Lab system for the participation in the constrained run of the EVALITA 2016 SENTIment POLarity Classification Task (Barbieri et al., 2016). The system extends the approach in (Passaro et al., 2014) with emotive features extracted from ItEM (Passaro et al., 2015; Passaro and Lenci, 2016) and FB-NEWS15 (Passaro et al., 2016).

Questo articolo descrive il sistema sviluppato all’interno del CoLing Lab per la partecipazione al task di EVALITA 2016 SENTIment POLarity Classification Task (Barbieri et al., 2016). Il sistema estende l’approccio descritto in (Passaro et al., 2014) con una serie di features emotive estratte da ItEM (Passaro et al., 2015; Passaro and Lenci, 2016) and FB-NEWS15 (Passaro et al., 2016).

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Extrait du texte

1 Introduction

Social media and microblogging services are extensively used for rather different purposes, from news reading to news spreading, from entertainment to marketing. As a consequence, the study of how sentiments and emotions are expressed in such platforms, and the development of methods to automatically identify them, has emerged as a great area of interest in the Natural Language Processing Community. Twitter presents many linguistic and communicative peculiarities. A tweet, in fact, is a short informal text (140 characters), in which the frequency of creative punctuation, emoticons, slang, specific terminology, abbreviations, links and hashtags is higher than in other domains and platforms. Twitter users post messages from many different media, including their smartphones, and they “tweet” about a great variety of topics, unlike what can be observed in other sites, which appear to be tailored to a specific group of topics (Go et al., 2009).

The paper is organized as foll...

Auteurs

CoLing Lab, Dipartimento di Filologia, Letteratura e Linguistica University of Pisa (Italy) - lucia.passaro@for.unipi.it

CoLing Lab, Dipartimento di Filologia, Letteratura e Linguistica University of Pisa (Italy) - alessandro.bondielli@gmail.com

CoLing Lab, Dipartimento di Filologia, Letteratura e Linguistica University of Pisa (Italy) - alessandro.lenci@unipi.it

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