Subjective Business Polarization: Sentiment Analysis meets Predictive Modeling

The growth of Internet and the information technology has generated big changes in subjects communication, that, nowadays, oc- curs through social media or via thematic forums. This produced a surge of information that is freely available: it offers the possibility to com- panies to evaluate their credibility and to monitor the ”mood” of their markets. The application of Sentiment Analysis (SA) has been proposed in order to extract, via objective rules, positive or negative opinions from (unstructured) texts. Communication literature, instead, highlights how such polarization derives from a subjective evaluations of the texts by the receivers. In business applications the receiver (i.e. marketing manager) is leaded by the values and the mission of the company. In our paper we propose a strategy to fit brand image and company values with a subjective SA, a probabilistic Kernel classifier has been employed to get discrimination rule and to rank classification results.