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챗봇 데이터에 나타난 우울 담론의 범주와 특성의 이해

Understanding the Categories and Characteristics of Depressive Moods in Chatbot Data

차미영|차지영|정찬이|백금희|진효진|최정회
한국정보처리학회|2022.09|정보처리학회 논문지 (The Transactions of the Korea Information Processing Society)|제11권 제9호|pp. 381-390

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키워드

정신건강챗봇ChatbotMental Health우울 담화우울 정서Depressive DiscourseDepressive Moods
주제분류컴퓨터학

초록

자연어처리 기술과 비대면 문화의 확산과 더불어 챗봇의 사용 증가세가 가파르며, 챗봇의 용도 또한 일상 대화와 소비자 응대를 넘어서 정신건강을 위한 용도로 확장하고 있다. 챗봇은 익명성이 보장된다는 점에서 사용자들이 우울감에 관해 이야기하기 적합한 서비스이다. 그러나 사용자가작성한 문장들을 분석해 우울 담론의 유형과 특성을 파악하는 연구들은 주로 소셜 네트워크 데이터를 대상으로 했다는 한계점이 존재하며, 실제환경에서 사용되는 챗봇과 상호작용한 데이터를 분석한 연구는 찾아보기 힘들다. 이 연구에서는 챗봇-사람의 상호작용 데이터에서 무작위로 추출한‘우울’과 관련된 대화 데이터를 토픽 모델링 방법과 텍스트마이닝 기법으로 분석하여 채팅에서의 우울 관련 담론의 특성을 파악하였다. 또한, 챗봇에서 빈번히 나타나는 ‘우울’ 담론의 범주와 트위터 ‘우울’ 담론의 범주의 차이점을 비교하였다. 이를 통해 챗봇 데이터의 ‘우울’ 대화만의 특징을파악하고, 적절한 심리지원 정보를 제공하는 챗봇 서비스를 위한 시사점과 향후 연구 방향에 대해 논의한다.

Abstract

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbotshave been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mentalhealth. 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 usedchatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing thechats, including the word ‘depress,’ using the topic modeling algorithm and the text-mining technique. Moreover, we compared itscharacteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenuesfor future research based on the study findings.

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