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MIB at SemEval-2016 Task 4a: Exploiting lexicon-based features for sentiment analysis in Twitter

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.

 


Semantic Evaluation 2016 (SemEval), San Diego, CA, 2016

Autori IIT:

Vittoria Cozza

Foto di Vittoria Cozza

Tipo: Contributo in atti di convegno
Area di disciplina: Information Technology and Communication Systems

File: S16-1019.pdf

Attività: Bolle dell'informazione e rilevamento di falsi in rete