A Comparison of Lexicon-based Approaches for Sentiment Analysis of Microblog Posts.
Cataldo Musto, Giovanni Maria Semeraro, Marco Polignano · 2014
Abstract. The exponential growth of available online information pro-vides 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 tech-niques, 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 clas-sification 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 effectiveness of the approach was evaluated against two state-of-the-art datasets. Pre-liminary results provide interesting outcomes and pave the way for future research in the area.