A deep learning semantic approach to emotion recognition using the IBM watson bluemix alchemy language

Abstract

Sentiment analysis and emotion recognition are emerging research fields of research that aim to build intelligent systems able to recognize and interpret human emotions. Due to the applicability of these systems to almost all kinds of markets, also the interest of companies and industries is grown in an exponential way in the last years and a lot of frameworks for programming these systems are introduced. IBM Watson is one of the most famous and used: it offers, among others, a lot of services for Natural Language Processing. In spite of broad-scale multi-language services, most of functions are not available in a lot of “secondary” languages (like Italian). The main objective of this work is to demonstrate the feasibility of a translation-based approach to emotion recognition in texts written in “secondary” languages. We present a prototypical system using IBM Watson to extract emotions from Italian text by means of Bluemix Alchemy Language. Some preliminary results are shown and discussed in order to stress pro and cons of the approach.