We combined a spoken dialog system that we developed to deliver brief health interventions with the fully autonomous humanoid robot (NAO). The dialog system is based on a framework facilitating Markov decision processes (MDP). It is optimized using reinforcement learning (RL) algorithms with data we collected from real user interactions. The system begins to learn optimal dialog strategies for initiative selection and for the type of confirmations that it uses during theinteraction. The health intervention, delivered by a 3D character instead of the NAO, has already been evaluated, with positive results in terms of task completion, ease of use, and future intention to use the system.  The current spoken dialog system for the humanoid robot is a novelty and exists so far as a proof ofconcept.

There is an increasing interest for valence and emotion sensing using a variety of signals. Text, as a communication channel, gathers a substantial amount of interest for recognizing its underlying sentiment (valence or polarity), affect or emotion (e.g. happy, sadness). We consider recognizing the valence of a sentence as a prior task to emotion sensing. In this article, we discuss our approach to classify sentences in terms of emotional valence. Our supervised system performs syntactic and semantic analysis for feature extraction. It processes the interactions between words in sentences by using dependency parse trees, and it can decide the current polarity of named-entities based on on-the-fly topic modeling. We compared 3 rule-based approaches and two supervised approaches (i.e. Naive Bayes and Maximum Entropy). We trained and tested our system using the SemEval-2007 affective text dataset, which contains news headlines extracted from news websites. Our results show that our systems outperform the systems demonstrated in SemEval-2007.