This study aims to analyze the relationship between the sociocognitive skills of a group of children and adolescents with autism spectrum disorder (ASD) at verbal level 1, the variability of the therapist’s heart rate (HRV), and the conversational turn-taking during online psychotherapy sessions. Initially, we assessed the intelligence, narrative, and behavioral characteristics of the participants. We videotaped the online sessions and recorded the therapist’s HRV via a smart wireless sensor. Finally, we analyzed the video sessions using an observation system and the therapist’s HRV using the Poincaré technique. The results show that the patients’ communicative intention was related to their narrative, intellectual and social competencies. Furthermore, the turn-taking between the therapist and the participant was associated with the patient’s emotional and behavioral difficulties. On the other side, the therapist’s heart rate variability (HRV) was related to the synchrony between the therapist and the participant with more significant stress on the therapist, when he shared and expanded the conversation with the patient, and when the patient broadened and shared the conversation with the therapist.

Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.