This work presents our team solution for task 4a (Message Polarity Classification) at the SemEval 2016 challenge. Our experiments have been carried out over the Twitter dataset provided by the challenge. We follow a supervised approach, exploiting a SVM polynomial kernel classifier trained with the challenge data. The classifier takes as input advanced NLP features. This paper details the features and discusses the achieved results.
MIB at SemEval-2016 Task 4a: Exploiting lexicon-based features for sentiment analysis in Twitter
Petrocchi M;Cozza V
2016
Abstract
This work presents our team solution for task 4a (Message Polarity Classification) at the SemEval 2016 challenge. Our experiments have been carried out over the Twitter dataset provided by the challenge. We follow a supervised approach, exploiting a SVM polynomial kernel classifier trained with the challenge data. The classifier takes as input advanced NLP features. This paper details the features and discusses the achieved results.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.
