• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.115-124
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• 2023

This study explores the intersection of literary studies and artificial intelligence (AI), focusing on the common theme of human emotions to foster complementary advancements in both fields. By adopting a comparative perspective, the paper investigates emotion as a shared focal point, analyzing various emotion-related concepts from both literary and AI perspectives. Despite the scarcity of research on the fusion of AI and literary studies, this study pioneers an interdisciplinary approach within the humanities, anticipating future developments in AI. It proposes that literary sensibility can contribute to AI by formalizing subjective literary emotions, thereby enhancing AI's understanding of complex human emotions. This paper's methodology involves the terminology-centered extraction of emotions, aiming to blend subjective imagination with objective technology. This fusion is expected to not only deepen AI's comprehension of human complexities but also broaden literary research by rapidly analyzing diverse human data. The study emphasizes the need for a collaborative dialogue between literature and engineering, recognizing each field's limitations while pursuing a convergent enhancement that transcends these boundaries.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.35-46
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• 2019

Artificial intelligence (AI) technology has been evolving to recognize and learn the languages, voice tones, and facial expressions of users so that they can respond to users' emotions in various contexts. Many AI-based services of particular importance in communications with users provide emotional interaction. However, research on nonverbal interaction as a means of expressing emotion in the AI system is still insufficient. We studied the effect of lighting on users' emotional interaction with an AI device, focusing on color and flickering motion. The AI device used in this study expresses emotions with six colors of light (red, yellow, green, blue, purple, and white) and with a three-level flickering effect (high, middle, and low velocity). We studied the responses of 50 men and women in their 20s and 30s to the emotions expressed by the light colors and flickering effects of the AI device. We found that each light color represented an emotion that was largely similar to the user's emotional image shown in a previous color-sensibility study. The rate of flickering of the lights produced changes in emotional arousal and balance. The change in arousal patterns produced similar intensities of all colors. On the other hand, changes in balance patterns were somewhat related to the emotional image in the previous color-sensibility study, but the colors were different. As AI systems and devices are becoming more diverse, our findings are expected to contribute to designing the users emotional with AI devices through lighting.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.206-209
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• 2003

본 논문에서는 퍼지추론과 면역 네트워크의 세 가지 메커니즘을 바탕으로 인간의 외부 자극(색상정보)에 따른 내부 감성상태를 인식할 수 있는 방법을 제안한다. 인간의 내부 감성상태는 심리학에서 많이 사용하는 색채심리를 바탕으로 추론을 하였으며 추론된 값은 색상 정보의 정도에 따른 감성상태이다. 이러한 감성상태의 값들 간에 유사성을 계산하여 면역 네트워크에 세 가지 메커니즘에 적용하여 인공적인 감성상태를 인식할 수 있는 방법을 나타내었다.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.313-316
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• 2020

감성 분석은 문장의 감성을 분석해 긍정 또는 부정으로 분류하는 작업을 의미한다. 문장에 담긴 감성을 파악해야 하기 때문에 문장 전체를 이해하는 것이 중요하다. 그러나 한 문장에 긍정과 부정의 이중 극성이 동존하는 문장은 감성 분석에 혼동이 생길 수 있다. 본 논문에서는 이와 같은 문제를 해결하기 위해 단어의 감성 점수 예측을 통해 감성 단어 등장 순서를 고려한 감성 분석 모델을 제안한다. 또한 최근 다양한 자연어 처리 분야에서 좋은 성능을 보이는 사전 학습 언어 모델을 활용한다. 실험 결과 감성 분석 정확도 90.81%로 기존 모델들에 비해 가장 좋은 성능을 보였다.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.269-275
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• 2022

As artificial intelligence services targeting humans increase, social demands are increasing that artificial intelligence should also be made on an ethical basis. Following this trend, the government and businesses are preparing policies and norms related to artificial intelligence ethics. In order to establish reasonable policies and norms, the first step is to understand the public's perceptions. In this paper, social data and news comments were collected and analyzed to understand the public's perception related to artificial intelligence and ethics. Interest analysis, emotional analysis, and discourse analysis were performed and visualized on the collected datasets. As a result of the analysis, interest in "artificial intelligence ethics" and "artificial intelligence" favorability showed an inversely proportional correlation. As a result of discourse analysis, the biggest issue was "personal information leakage," and it also showed a discourse on contamination and deflection of learning data and whether computer-made artificial intelligence should be given a legal personality. This study can be used as data to grasp the public's perception when preparing artificial intelligence ethical norms and policies.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.9-19
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• 2019

The effects of artificial intelligence technology is social science research as research on the impact on industry and changes in daily life, etc. This means that developing 'emotion AI' will prepare 'next-generation 3D-vector-sensitive AI'. This suggests the main keywords of the tertiary AI decision-making power. Particularly important results will be achieved because of the importance of current unethical learning and the implementation of decision-making systems that reflect ethical value judgments. This is a data based simulation, and required (1)Available data, (2)the technology for the goal of simulation. This takes into account the general content of the intended simulation based research. Currently, existing researches focus on meaningful research motivation, but this study presents the direction of technology. So, empirical analysis is consistent with the decision-making power of each country vs. new technology firms for AI on ehtic responsibility. As a result, there is a need for a concrete contribution and interpretation that can be achieved for the ethic Responsibility, on the technical side of AI / ML. In AI decision making, analytic power of human empathy should be included tech own trust.

• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.527-535
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• 2017

In this study, we analyzed the image of artificial intelligence recognized by elementary students using semantic differential scale. First, we extracted 23 pairs of image adjectives related to perception of artificial intelligence. Adjectives were classified into three types related to recognition, emotion and ability and 827 elementary students were examined. Image factors were classified into four factors: convenience, technological progress, human-friendliness, and concern. As a result, they showed a clear image that artificial intelligence is clever, new, and complex but exciting. In comparison with variables, female students, coding experience and older students thought that artificial intelligence was more human-friendly and technological progressive.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.16-24
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• 2021

This paper introduces a smart aging service that helps the elderly lead a happy old age by actively utilizing IoT and AI technologies for the elderly who are increasing rapidly as they enter the aging society. In particular, we propose a future-oriented, age-friendly well-being support system that breaks away from the existing welfare concept to solve the aging problem but leads to a paradigm shift toward building a vibrant aging society by protecting from emergency and satisfying emotions. By introducing IoT and AI, it judges the life situation and emotional state from the living information of the elderly can respond to emergencies and suggest meetings as a change of mood and give an emotional comfort. Since the proposed system uses artificial intelligence techniques to determine the degree of depression when inputting information such as pulse-rate, dangerous word usage, and external communication, I think it showed the feasibility of the new concept of wellbeing support system that is totally different from conventional wellbeing concept of health-care.

• Smart Media Journal
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• v.10 no.1
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• pp.116-123
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• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.