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.

Emotions Extracted from Text vs. True Emotionsxe2x80x93An Empirical Evaluation in SE Context Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However, how well these extracted emotion states reflect peopleu0027s real emotions has not been thoroughly investigated. In this paper, we report a multilevel, longitudinal empirical study with 82 individual members in 27 project teams. We collected their selfreported retrospective emotion states on a weekly basis during their yearlong projects and also extracted corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using textbased algorithms often do not well reflect the dynamics of selfreported retrospective emotions. Besides, the extracted emotions match selfreported retrospective emotions better at the teamlevel. Our results also suggest that individual personalities and the teamu0027s emotion display norms significantly impact the matchmismatch. Our results should warn the research community about the limitations and challenges of applying textbased emotion recognition tools in SE research.