A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this paper, we present the design and evaluation of an emotion-aware chatbot that conducts experience sampling in an empathetic manner. We evaluate it through a human-subject experiment with N=39 participants over the course of a week. Our results show that extraverts preferred the emotion-aware chatbot significantly more than introverts. Also, participants reported a higher percentage of positive mood reports when interacting with the empathetic bot. Finally, we provide guidelines for the design of emotion-aware chatbots for potential use in mHealth contexts.
While these efforts have tried to address the issue of user engagement with ESM, the tone of delivery of ESM and its implications are not well studied. Specifically, ESM goes beyond being purely a method for capturing data. It provides a means for reflecting on one's past more objectively, which has the potential to improve psychological wellbeing and personal growth @cite_30 . ESM is widely used in applications for improving mental health and wellbeing. Recently, personal assistants, chatbots, and virtual assistants (VAs) are are being used more often for conducting experience sampling. Therefore, it becomes imperative to study the characteristics of such an agent delivering ESM.
@cite_30
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The experience-sampling (ESM) technique is a method in which recording of feelings and activities is done on-line at the moment, either at randomly selected moments or at predetermined times. This method has the advantage of being able to not only assess people’s general feelings, but to link feelings with situations, times of day, and other circumstances. Thus, ESM provides a powerful way of moving beyond simple questions about who is “happy” and who is not, to more intricate questions about when and why people experience positive and negative feelings. Compared to retrospective reports of feelings, ESM is less influenced by memory biases. ESM also allows researchers to analyze the patterning and relationships of feelings as they unfold over time. Despite the strengths and promise of this method, there are also limitations. For example, the heavy demand placed on research participants means that the sample might be biased toward highly conscientious individuals, and repeatedly reporting one’s moods might itself influence feelings. How to analyze ESM data is discussed, including the issue of how to aggregate momentary feelings into global measures of the average subjective well-being of individuals. To understand the dynamics of mental health, it is essential to develop measures for the frequency and the patterning of mental processes in every-day-life situations. The Experience-Sampling Method (ESM) is an attempt to provide a valid instrument to describe variations in self-reports of mental processes. It can be used to obtain empirical data on the following types of variables: (a) frequency and patterning of daily activity, social interaction, and changes in location; (b) frequency, intensity, and patterning of psychological states, i.e., emotional, cognitive, and conative dimensions of experience; (c) frequency and patterning of thoughts, including quality and intensity of thought disturbance. The article reviews practical and methodological issues of the ESM and presents evidence for its short-and long-term reliability when used as an instrument for assessing the variables outlined above. Voice-controlled intelligent personal assistants, such as Cortana, Google Now, Siri and Alexa, are increasingly becoming a part of users' daily lives, especially on mobile devices. They introduce a significant change in information access, not only by introducing voice control and touch gestures but also by enabling dialogues where the context is preserved. This raises the need for evaluation of their effectiveness in assisting users with their tasks. However, in order to understand which type of user interactions reflect different degrees of user satisfaction we need explicit judgements. In this paper, we describe a user study that was designed to measure user satisfaction over a range of typical scenarios of use: controlling a device, web search, and structured search dialogue. Using this data, we study how user satisfaction varied with different usage scenarios and what signals can be used for modeling satisfaction in the different scenarios. We find that the notion of satisfaction varies across different scenarios, and show that, in some scenarios (e.g. making a phone call), task completion is very important while for others (e.g. planning a night out), the amount of effort spent is key. We also study how the nature and complexity of the task at hand affects user satisfaction, and find that preserving the conversation context is essential and that overall task-level satisfaction cannot be reduced to query-level satisfaction alone. Finally, we shed light on the relative effectiveness and usefulness of voice-controlled intelligent agents, explaining their increasing popularity and uptake relative to the traditional query-response interaction. Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.