Model of Multiturn Dialogue in Emotional Chatbot The intent recognition and natural language understanding of multiturn dialogue is key for the commercialization of chatbots. Chatbots are mainly used for the processing of specific tasks, and can introduce products to customers or solve related problems, thus saving human resources. Text sentiment recognition enables a chatbot to know the userxe2x80x99s emotional state and select the best response, which is important in medical care. In this study, we combined the multiturn dialogue model and sentiment recognition model to develop a chatbot, that is designed for used in daily conversations rather than for specific tasks. Thus, the chatbot has the ability to provide the robotxe2x80x99s emotions as feedback while talking with a user. Moreover, it can exhibit different emotional reactions based on the content of the userxe2x80x99s conversation.

A Cognitive Model for Emotion Awareness in Industrial Chatbots Industrial applications are increasingly adopting conversational agents (chatbots) for tasks ranging from primitive conversation interfaces to intelligent human assistants. In this emerging field of research, there has been a strong drive towards modelling humanlike characteristics and behaviors in chatbots. However, only a limited number of research endeavors have focused on a chatbot for automatic characterization of enduser emotions. In order to address this limitation, we propose an artificial intelligence based cognitive model for emotion awareness in chatbots using Markov chains, word embedding, and Natural Language Processing. The proposed model is able to extract emotions from conversations, detect emotion transitions over time, predict realtime emotions and intelligently profile human participants based on their distinct emotional characteristics. We conducted experiments using a realworld enduser dataset to demonstrate the functionality of the proposed model. Results from experiments confirm the plausibility of this model for emotion awareness in industrial conversational agents.