In this paper we describe our approach to EVALITA 2014 SENTIment POLarity Classification (SENTIPOLC) task. We participated only in the Polarity Classification sub-task. By resorting to a wide set of general-purpose features qualifying the lexical and grammatical structure of a text, automatically created ad-hoc lexicons and existing free available resources, we achieved the second best accuracy.
Linguistically-motivated and Lexicon Features for Sentiment Analysis of Italian Tweets
Stefano Cresci;Felice Dell'Orletta;Maurizio Tesconi
2014
Abstract
In this paper we describe our approach to EVALITA 2014 SENTIment POLarity Classification (SENTIPOLC) task. We participated only in the Polarity Classification sub-task. By resorting to a wide set of general-purpose features qualifying the lexical and grammatical structure of a text, automatically created ad-hoc lexicons and existing free available resources, we achieved the second best accuracy.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
