• The Journal of the Korea Contents Association
• /
• v.19 no.4
• /
• pp.12-29
• /
• 2019

The purpose of this study is to develop the scenario for implementation of the rule-based counseling chatbot and to verify its usefulness. For this purpose, counseling scenario for exploring problems triggering depression and establishing early counseling relationships was built and user evaluations for the rule-base counseling chatbot were conducted on 10 counseling psychologists. As a result, clear counseling structuring, appropriate use of selective and narrative responses, and active listening were identified as important components of counseling chatbot. To the next step, the usefulness of counseling chatbot developed in the previous step was examined with 78 college students. As a result, active listening was found to be an important factor in improving user satisfaction and positive mood change. In addition, through qualitative analysis of user evaluation, the unique advantages of counseling chatbot were identified and the limitations of counseling chatbot that should be overcome were identified. Based on the results of the study, the implications of the study and suggestions for future research were discussed.

• Journal of Information Technology Services
• /
• v.20 no.3
• /
• pp.75-86
• /
• 2021

As artificial intelligence is actively studied, chatbot systems are being applied to various fields. In particular, many chatbot systems for psychological counseling have been studied that can comfort modern people. However, while most psychological counseling chatbots are studied as rule-base and deep learning-based chatbots, there are large limitations for each chatbot. To overcome the limitations of psychological counseling using such chatbots, we proposes a novel psychological counseling AI chatbot system. The proposed system consists of a GPT-2 model that generates output sentence for Korean input sentences and an Electra model that serves as sentiment analysis and anxiety cause classification, which can be provided with psychological tests and collective intelligence functions. At the same time as deep learning-based chatbots and conversations take place, sentiment analysis of input sentences simultaneously recognizes user's emotions and presents psychological tests and collective intelligence solutions to solve the limitations of psychological counseling that can only be done with chatbots. Since the role of sentiment analysis and anxiety cause classification, which are the links of each function, is important for the progression of the proposed system, we experiment the performance of those parts. We verify the novelty and accuracy of the proposed system. It also shows that the AI chatbot system can perform counseling excellently.

• Knowledge Management Research
• /
• v.20 no.4
• /
• pp.99-118
• /
• 2019

Online financial counseling services are increasingly expanding with the rise of artificial intelligence-based chatbots. It is very important to examine the effects of emoticons noted as alternatives for communicating emotions in online communication between consumers and companies. In this paper, we examine how the use of emoticons affects the consumer's response and investigate the moderating roles of type of counseling agents (human vs. chatbot) and the consumer's subjective financial knowledge. The results show that the use of emoticon in the conversation brings a positive effect on the consumer's intention to use of online chat counseling service. When participants had relatively low subjective financial knowledge, they had higher intention to use online chat counseling services with emoticons only when the agent type was chatbot. When the type of counseling agent was human, this positive effect of the emoticon did not occur. On the other hand, when participants had relatively high subjective financial knowledge, they had higher intention to use online chat counseling service with emoticons only when the agent type was human. This study contributes to providing practical implications to build online chat counseling service using chatbot in the financial industry by studying users' intention depending on the type of agents and the level of their subjective knowledge.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.07a
• /
• pp.429-430
• /
• 2017

본 논문은 챗봇을 활용하여 반복적인 입시 질문을 응답하는 시스템에 설계하였다. 1회성 응답 대신 문맥을 기억하여 사용자의 의도를 파악하고 자세한 응답을 제공한다. 또한, 한글 처리와 입시 관련 데이터베이스 연동을 통해 일반화 할 수 있는 유연성을 갖는 구조이며, 반복적인 질의응답을 챗봇으로 대신할 수 있을 것으로 기대된다.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.118-129
• /
• 2018

In AI chatbot service via a messenger, a profile image of the chatbot is the first thing that users see to communicate with the chatbot. This profile image not only manages an impression about the profile owner in SNS on followers, but also makes an important impression about chatbot services on users. Thus motivated, this study investigates proper profile images tailored for the types of chatbot services and users. Specifically, I reviewed the preferred images and expressions of chatbots for each purpose of chatbot service. Then, in a case study, I collected and analyzed the representative chatbot profile images for the purpose of fun and counseling. The profile images are categorized as robot, human, animal, and abstract images. Based on these categories, I surveyed the preferred profile image of the chatbot service in either the text type or image type alternatives. For the purpose of fun, in the text version, I found that both men and women preferred a human image to others. However, in the image version, men preferred woman and robot images while women preferred cute animation character and robot images. For counseling services, both men and women preferred woman and animal images most, which is similar to the results of the text version of questionnaires as well. While both genders consistently preferred real photo images, women tend to like abstract images more than men do. I expect that the results of this study would be useful to develop the proper profile images of AI chatbot for each service purpose.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.01a
• /
• pp.185-186
• /
• 2020

최근 제품을 주문하거나 상품을 조회하는 등의 간단한 상담을 챗봇을 이용하여 자동화하는 온라인 쇼핑몰들이 늘어나고 있다. 이는 고객을 상담하는 상담원의 업무를 줄여줄 뿐 아니라 고객 상담을 즉각적이고 효율적으로 진행할 수 있다. 또한 사용자의 입장에서 챗봇은 처음 이용하는 사람도 사용하기가 쉽고, 상담원과의 연결까지 기다리지 않고 사용자가 원하는 시간에 커뮤니케이션이 가능하고, 기업 측면에서는 인건비가 감소되고 고객관리가 용이해진다는 장점이 있다. 그러나 챗봇은 주어진 질문에만 대답할 수 있고, 처음 메뉴를 파악하기 힘들다는 단점이 있다. 따라서 본 논문에서는 카카오톡 오픈빌더를 사용하여 질문의도를 파악하는 intent와 entity를 추출한 뒤 딥러닝을 통해 체계적으로 학습을 진행한다. 이를 통해 주어지지 않은 질문들을 파악한다. 또한, 오픈빌더의 시나리오 선택 기능을 활용하여 초기에 선택할 수 있는 메뉴를 파악하기 쉽도록 구현하였다. 사용자는 본 논문에서 제안하는 챗봇을 통해 사용자는 상담에 필요한 도움을 받을 수 있을 것으로 기대된다.

• The Journal of the Korea Contents Association
• /
• v.22 no.2
• /
• pp.255-269
• /
• 2022

In this study, it was necessary to examine consumer acceptance attitudes from an in-depth and multifaceted perspective at a time when the need for chatbot services in various industrial fields is increasing and being activated in earnest. Accordingly, this study conducted a structural equation model to examine not only the structural relationship between ease, usefulness, and playfulness among the main functions of chatbot services and their acceptance attitudes, but also whether there is a mediating effect of service value in the relationship. As a result of the main study of this study, it was identified that the relationship between the ease, usefulness, and playfulness factors, which are the main functional sub-factors of the chatbot service, and their acceptance attitude and service value had a statistically static influence relationship. Based on these research results, the main research conclusions suggest that when companies in various fields provide chatbot services in the future, it is necessary to clearly determine the influencing factors that can affect the chatbot service acceptance attitude and provide these services. Through this, it is expected that the AI chatbot service will strengthen communication with consumers and establish itself as a customized and personalized counseling service.

• Journal of Korea Multimedia Society
• /
• v.24 no.2
• /
• pp.320-327
• /
• 2021

We aim to investigate the effects of task type and user gender on the expected gender of chatbots. We conducted an online study of 381 participants who selected the gender (female, male, or neutral) for chabots performing six different tasks. Our results indicate that users expect human- gendered chatbots for all tasks and that the expected gender of a chatbot is significantly different depending on the task type. Users expected chatting, counseling, healthcare and clerical work to be done by female chatbots; professional and customer service work were expected to be done by male chatbots. A tendency for participants to prefer chatbots of the same-gendered as themselves is revealed in several tasks for both male and female users. However, this homophily tendency is stronger for female users. We conclude by suggesting practical guidelines for designing chatbot services that reflect user expectations.

• Journal of Internet Computing and Services
• /
• v.20 no.2
• /
• pp.93-100
• /
• 2019

Recently, in various business fields, companies are concentrating on providing chatbot services to various environments by adding artificial intelligence to existing messenger platforms. Organizations in the field of job placement also require chatbot services to improve the quality of employment counseling services and to solve the problem of agent management. A text-based general chatbot classifies input user sentences into learned sentences and provides appropriate answers to users. Recently, user sentences inputted to chatbots are inputted as short texts due to the activation of social network services. Therefore, performance improvement of short text classification can contribute to improvement of chatbot service performance. In this paper, we propose T-EBOW (Translation-Extended Bag Of Words), which is a method to add translation information as well as concept information of existing researches in order to strengthen the short text classification for employment chatbot. The performance evaluation results of the T-EBOW applied to the machine learning classification model are superior to those of the conventional method.

• Smart Media Journal
• /
• v.10 no.4
• /
• pp.21-27
• /
• 2021

Recently, the importance of counseling is increasing due to the Corona Blue caused by COVID-19. Also, with the increase of non-face-to-face services, researches on chatbots that have changed the counseling media are being actively conducted. In non-face-to-face counseling through chatbot, it is most important to accurately understand the client's emotions. However, since there is a limit to recognizing emotions only in sentences written by the client, it is necessary to recognize the dimensional emotions embedded in the sentences for more accurate emotion recognition. Therefore, in this paper, the vector and sentence VAD (Valence, Arousal, Dominance) generated by learning the Word2Vec model after correcting the original data according to the characteristics of the data are learned using a deep learning algorithm to learn the multi-dimensional We propose an emotion recognition model. As a result of comparing three deep learning models as a method to verify the usefulness of the proposed model, R-squared showed the best performance with 0.8484 when the attention model is used.