Characterization of sentiment groups on Twitter

Abstract

Opinion Mining is the field of computational study of people’s emotional behavior expressed in text. The purpose of this article is to introduce a new framework for characterization of the groups of emotions extracted from tweet data. In contrast to supervised learning, the problem of clustering characterization in the context of opinion mining based on unsupervised learning is challenging, because label information is not available. The proposed framework uses topological unsupervised learning and hierarchical clustering, each cluster being associated to a prototype and a weight vector, reflecting the relevance of the data belonging to each cluster. The proposed framework requires simple computational techniques and is based on the double local weighting self-organizing map (dlw-SOM) model and Hierarchical Clustering. The proposed framework has been used on a real dataset issued from the tweets collected during the 2012 French election campaign.