Sentiment Analysis for Dynamic User Preference Inference in Spoken Dialogue Systems

Many current spoken dialogue systems for search are domainspecific and do not take into account the preferences of the user and his opinion about the proposed items. In order to provide a more personalized answer, tailored to the user needs, in this paper we propose a spoken dialogue system where user interests are expressed as scores in modular ontologies and his sentiment about the system propositions is considered. This approach allows for a dynamic and evolving representation of user interests. In fact, in order to improve the performance of the detection mechanism of users preferences, we propose a hybrid model which also makes use of a sentiment analysis module to detect the opinion of the user with respect to the proposition of the system. This allows the system to leverage the degree of user satisfaction and improve the overall recommendation mechanism being more precise about the expressed user interest. An evaluation on a representative set of dialogues is presented and highlights both the validity and the reliability of the proposed preference inference mechanism.