The Blind Men and the Elephant Revisited

Isbister, Katherine; Doyle, Patrick

doi:10.1007/1-4020-2730-3_1

Cited by 24 publications

(9 citation statements)

References 17 publications

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“…Thus far, iCBT seems more effective when it includes guidance than when guidance is absent (Richards and Richardson, 2012: Andersson and Titov, 2014). The present study is part of a project in which we explore whether we can bridge the gap between guided and unguided interventions, either completely or partially, through automated support by embodied conversational agents (ECAs), computer-generated characters that can simulate verbal and non-verbal behaviors similar to those used in human face-to-face conversations (Isbister and Doyle, 2004).…”

Section: Introductionmentioning

confidence: 99%

Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study

Provoost

Ruwaard

Breda³

et al. 2019

Front. Psychol.

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Introduction Sentiment analysis may be a useful technique to derive a user’s emotional state from free text input, allowing for more empathic automated feedback in online cognitive behavioral therapy (iCBT) interventions for psychological disorders such as depression. As guided iCBT is considered more effective than unguided iCBT, such automated feedback may help close the gap between the two. The accuracy of automated sentiment analysis is domain dependent, and it is unclear how well the technology is applicable to iCBT. This paper presents an empirical study in which automated sentiment analysis by an algorithm for the Dutch language is validated against human judgment. Methods A total of 493 iCBT user texts were evaluated on overall sentiment and the presence of five specific emotions by an algorithm, and by 52 psychology students who evaluated 75 randomly selected texts each, providing about eight human evaluations per text. Inter-rater agreement (IRR) between algorithm and humans, and humans among each other, was analyzed by calculating the intra-class correlation under a numerical interpretation of the data, and Cohen’s kappa, and Krippendorff’s alpha under a categorical interpretation. Results All analyses indicated moderate agreement between the algorithm and average human judgment with respect to evaluating overall sentiment, and low agreement for the specific emotions. Somewhat surprisingly, the same was the case for the IRR among human judges, which means that the algorithm performed about as well as a randomly selected human judge. Thus, considering average human judgment as a benchmark for the applicability of automated sentiment analysis, the technique can be considered for practical application. Discussion/Conclusion The low human-human agreement on the presence of emotions may be due to the nature of the texts, it may simply be difficult for humans to agree on the presence of the selected emotions, or perhaps trained therapists would have reached more consensus. Future research may focus on validating the algorithm against a more solid benchmark, on applying the algorithm in an application in which empathic feedback is provided, for example, by an embodied conversational agent, or on improving the algorithm for the iCBT domain with a bottom-up machine learning approach.

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Section: Introductionmentioning

confidence: 99%

Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study

Provoost

Ruwaard

Breda³

et al. 2019

Front. Psychol.

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“…For example, in their book dedicated to the evaluation of ECAs, [45] point out the difference between micro-level evaluation focused on a single feature of the ECA and macro-level evaluation focused on the global contribution of the ECA to an application. In the same book, [38] provide a taxonomy of macrolevel dimensions to evaluate in an ECA, such as believability or sociability, with corresponding evaluation criteria. Another evaluation issue concerns the target users of the Andersen system, i.e.…”

Section: Discussionmentioning

confidence: 99%

Fusion of children's speech and 2D gestures when conversing with 3D characters

et al. 2006

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractMost existing multi-modal prototypes enabling users to combine 2D gestures and speech input are task-oriented. They help adult users solve particular information tasks often in 2D standard Graphical User Interfaces. This paper describes the NICE Andersen system, which aims at demonstrating multi-modal conversation between humans and embodied historical and literary characters. The target users are 10-18 years old children and teenagers. We discuss issues in 2D gesture recognition and interpretation as well as temporal and semantic dimensions of input fusion, ranging from systems and component design through technical evaluation and user evaluation with two different user groups. We observed that recognition and understanding of spoken deictics were quite robust and that spoken deictics were always used in multimodal input. We identified the causes of the most frequent failures of input fusion and suggest possible improvements for removing these errors. The concluding discussion summarises the knowledge provided by the NICE Andersen system on how children gesture and combine their 2D gestures with speech when conversing with a 3D character, and looks at some of the challenges facing theoretical solutions aimed at supporting unconstrained speech/2D gesture fusion. r

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“…Designing CAs requires a collective effort from different disciplines including the social, cognitive and computer sciences [42]. Combining all these knowledge domains and features in one CA is challenging, which suggests the applicability of designing domaindependent CAs that address a particular problem in a particular context [43]. Several taxonomies have been proposed to guide the design of CAs [43][44][45].…”

Section: An Introduction To Conversational Agentsmentioning

confidence: 99%

In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence

Abdulrahman

Richards

2021

MTI

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Conversational agents offer promise to provide an alternative to costly and scarce access to human health providers. Particularly in the context of adherence to treatment advice and health behavior change, they can provide an ongoing coaching role to motivate and keep the health consumer on track. Due to the recognized importance of face-to-face communication and establishment of a therapist-patient working alliance as the biggest single predictor of adherence, our review focuses on embodied conversational agents (ECAs) and their use in health and well-being interventions. The article also introduces ECAs who provide explanations of their recommendations, known as explainable agents (XAs), as a way to build trust and enhance the working alliance towards improved behavior change. Of particular promise, is work in which XAs are able to engage in conversation to learn about their user and personalize their recommendations based on their knowledge of the user and then tailor their explanations to the beliefs and goals of the user to increase relevancy and motivation and address possible barriers to increase intention to perform the healthy behavior.

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The Blind Men and the Elephant Revisited

Cited by 24 publications

References 17 publications

Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study

Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study

Fusion of children's speech and 2D gestures when conversing with 3D characters

In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence

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