ABSTRACT
Due to the coronavirus pandemic international conflicts, dramatic changes of daily living have been enforced, including new ways of providing patient assistance, based on artificial intelligence. The influence of these changes on people's mental health is still insufficiently analyzed and explored. Chatbots like Woebot, Wysa and Tess are gaining popularity, being attractive and easy to use. These achievements led us to develop a new application, being still in the testing phase, which has a positive impact on mental healthcare issues. It is a conversational system capable to diagnose people's negative, depressive, and anxious emotions during chatting, and to act as a psychological therapist and virtual friend. The proposed system, throughout the conversation, succeeds to decrease the patient's insecurity sentiments, by comforting their mood. In fact, an intelligent assistant for different mental health issues like stress, anxiety and depression, could become a very helpful information system.
ABSTRACT
Nowadays, the interest in data modelling from the spatial-temporal perspective is constantly increasing. Moreover, a wide variety of applications, such as social network data, need to be done to study spatiotemporal patterns. In general, however, these patterns are highly complex and challenging, so it is a demanding process to analyze or to classify them as the conventional context in various types of event data. In order to analyze the traffic viral within the text from the perspective of impressive negative effects, we should spatial-temporally localize the event and geographical regions and give a semantically interpreting of what happened. We propose a review of the best models and techniques applied for social media data processing to formalize a novel theory of action and time. This investigation intends to draw the basic knowledge level over which research intended to decipher in texts the occurrence of events, together with their involved characters, and their relationship with time and space.
