In this paper we describe our approach to EVALITA 2014 SENTIment POLarity Classification (SENTIPOLC) task. We participated only in the Polarity Classification subtask. 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.
Cimino, A., Cresci, S., Dell’Orletta, F., & Tesconi, M. (2014). Linguistically-motivated and lexicon features for sentiment analysis of italian tweets. 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2014)
Written by STEFANO CRESCI
Published in
Elenco Pubblicazioni - Publications
