A comparison of Lexicon-based approaches for Sentiment Analysis of microblog posts

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.