Abstract

The exponential growth of available online information provides computer scientists with many new challenges and opportunities. A recent trend is to analyze people feelings, opinions and orientation about facts and brands: this is done by exploiting Sentiment Analysis techniques, whose goal is to classify the polarity of a piece of text according to the opinion of the writer. In this paper we propose a lexicon-based approach for sentiment classication of Twitter posts. Our approach is based on the exploitation of widespread lexical resources such as SentiWordNet, WordNet-Affect, MPQA and SenticNet. In the experimental session the eectiveness of the approach was evaluated against two state-of-the-art datasets. Preliminary results provide interesting outcomes and pave the way for future research in the area.

Autore Pugliese

• Musto Cataldo , Semeraro Giovanni

Tutti gli autori

• MUSTO C.;SEMERARO G.;POLIGNANO M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2014

ISSN

1613-0073

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

19

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile