A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation | IEEE Conference Publication | IEEE Xplore

A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation


Abstract:

There are early studies to attempt users for psychiatric counseling with chatbot. They lead to changes in drinking habit based on intervention approach via chat bot. The ...Show More

Abstract:

There are early studies to attempt users for psychiatric counseling with chatbot. They lead to changes in drinking habit based on intervention approach via chat bot. The application does not consider the user's psychiatric status through the conversations, continuous user monitoring, and ethical judgment in the intervention. We contend that more accurate and continuous emotion recognition gives better satisfaction to users who need mental health care. In addition, appropriate clinical psychological response based on ethical responses is as well. We suggest a conversational service for psychiatric counseling that is adapted methodologies to understand counseling contents based on of high-level natural language understanding (NLU), and emotion recognition based on multi-modal approach. The methodologies enable continuous observation of emotional changes sensitively. In addition, the case-based counseling response model that combines ethical judgment model provides a suitable response to clinical psychiatric counseling.
Date of Conference: 29 May 2017 - 01 June 2017
Date Added to IEEE Xplore: 03 July 2017
ISBN Information:
Electronic ISSN: 2375-0324
Conference Location: Daejeon, Korea (South)

I. Introduction

It has been a long research topic that machines recognize human emotions. Recently, many studies improves to recognize human emotions based on artificial intelligent (AI) methods. The studies train various emotion classification models from a lot of emotional-labeled data based on deep learning, such as convolution neural network [2], recurrent neural net-work [3], attention network [3], [4]. For the techniques are advanced, the training data are also diversified to image [2], video [2], [3], audio [4] and text [5]. Some studies [3], [5] com-bine the classification models using multi-modal classification based on hybrid approaches. The studies report significant results for emotion recognition. They are quite accurate to recognize the human emotions.

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References

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