Ranked WordNet graph for Sentiment Polarity Classification in Twitter

•The purpose of this article is to present a novel approach to Sentiment Polarity Detection in Twitter posts.•We propose a non-supervised and domain-independent solution to Sentiment Analysis in Twitter.•The method combines SentiWordNet scores with a random walk analysis of the concepts found in the text over the WordNet graph.•Several experiments have been performed in order to compare them with other approaches like machine learning solutions.

This paper presents a novel approach to Sentiment Polarity Classification in Twitter posts, by extracting a vector of weighted nodes from the graph of WordNet. These weights are used in SentiWordNet to compute a final estimation of the polarity. Therefore, the method proposes a non-supervised solution that is domain-independent. The evaluation of a generated corpus of tweets shows that this technique is promising.