Understanding the Categories and Characteristics of Depressive Moods in Chatbot Data


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 9, pp. 381-390, Sep. 2022
https://doi.org/10.3745/KTSDE.2022.11.9.381,   PDF Download:
Keywords: Chatbot, Depressive Discourse, Depressive Moods, Mental Health
Abstract

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word ‘depress,’ using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.


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Cite this article
[IEEE Style]
H. Chin, C. Jung, G. Baek, C. Cha, J. Choi, M. Cha, "Understanding the Categories and Characteristics of Depressive Moods in Chatbot Data," KIPS Transactions on Software and Data Engineering, vol. 11, no. 9, pp. 381-390, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.9.381.

[ACM Style]
HyoJin Chin, Chani Jung, Gumhee Baek, Chiyoung Cha, Jeonghoi Choi, and Meeyoung Cha. 2022. Understanding the Categories and Characteristics of Depressive Moods in Chatbot Data. KIPS Transactions on Software and Data Engineering, 11, 9, (2022), 381-390. DOI: https://doi.org/10.3745/KTSDE.2022.11.9.381.