522 research outputs found
Discovering the Hidden Facts of User-Dispatcher Interactions via Text-based Reporting Systems for Community Safety
Recently, an increasing number of safety organizations in the U.S. have
incorporated text-based risk reporting systems to respond to safety incident
reports from their community members. To gain a better understanding of the
interaction between community members and dispatchers using text-based risk
reporting systems, this study conducts a system log analysis of LiveSafe, a
community safety reporting system, to provide empirical evidence of the
conversational patterns between users and dispatchers using both quantitative
and qualitative methods. We created an ontology to capture information (e.g.,
location, attacker, target, weapon, start-time, and end-time, etc.) that
dispatchers often collected from users regarding their incident tips. Applying
the proposed ontology, we found that dispatchers often asked users for
different information across varied event types (e.g., Attacker for Abuse and
Attack events, Target for Harassment events). Additionally, using emotion
detection and regression analysis, we found an inconsistency in dispatchers'
emotional support and responsiveness to users' messages between different
organizations and between incident categories. The results also showed that
users had a higher response rate and responded quicker when dispatchers
provided emotional support. These novel findings brought significant insights
to both practitioners and system designers, e.g., AI-based solutions to augment
human agents' skills for improved service quality
Towards Emotion-Sensitive Conversational User Interfaces in Healthcare Applications
Perception of emotions and adequate responses are key factors of a successful conversational agent. However, determining emotions in a healthcare setting depends on multiple factors such as context and medical condition. Given the increase of interest in conversational agents integrated in mobile health applications, our objective in this work is to introduce a concept for analyzing emotions and sentiments expressed by a person in a mobile health application with a conversational user interface. The approach bases upon bot technology (Synthetic intelligence markup language) and deep learning for emotion analysis. More specifically, expressions referring to sentiments or emotions are classified along seven categories and three stages of strengths using treebank annotation and recursive neural networks. The classification result is used by the chatbot for selecting an appropriate response. In this way, the concerns of a user can be better addressed. We describe three use cases where the approach could be integrated to make the chatbot emotion-sensitive
Modelos de detección de emociones en texto y rostros para agentes conversacionales multimodales
El presente trabajo de investigación aborda la implementación, análisis y selección de
distintos modelos de redes neuronales recurrentes (RNN) y convolucionales (CNN)
para la detección de emociones en texto y rostros; los cuales pueden ser utilizados
como módulos adicionales en agentes conversacionales de tiempo real como son
chatbots o robots sociales. Los módulos de detección permiten a los agentes
conversacionales poder entender cómo se sienten las personas durante la interacción
con ellas; conociendo estos estados los agentes conversacionales pueden responder
empáticamente.
En primer lugar, se revisará la literatura sobre como los agentes conversacionales
buscan ser más empáticos, así como los métodos de detección de emociones mediante
distintos canales como texto y rostros. Luego, se procede a recolectar y pre-procesar
bases de datos públicas para el entrenamiento de los algoritmos seleccionados en base
a la literatura.
Finalmente, métricas tanto para la evaluación del rendimiento de predicción multiclase
(Accuracy, Precision, Recall y F1), como la velocidad de procesamiento (ej. Framesper-
second) son seleccionadas y analizadas para determinar cuáles son los mejores
algoritmos para implementar una aplicación de tiempo real
- …