• The Journal of the Korea Contents Association
• /
• v.16 no.5
• /
• pp.428-439
• /
• 2016

It becomes undeniable trends that offline shopping stores operate their own online shopping stores too. The multi-channel shopping stores like this allow consumers to have much choices to shop from either offline channel or online channel. This trend, however, also opens new research issues. Especially, we have found from literature survey that a new research model is necessary for more in-depth study of the consumer behavior analysis in the multi-channel trends like this, where those constructs such as offline trust, uncertainty, switching cost, and individual negative emotion are considered. It is noted, especially in the multi-channel environments, that uncertainty and switching cost need to be considered, and that individual tends to feel negative emotion much more. By relying on 406 valid questionnaires, we obtained empirical results such that switching cost and offline trust have a positive effect on continuance intention, and uncertainty tends to increase switching cost. Individual negative emotion also affects continuance intention significantly.

• Journal of the Korean Society for Library and Information Science
• /
• v.51 no.4
• /
• pp.121-138
• /
• 2017

As information users seek multimedia with a wide variety of information needs, information environments for multimedia have been developed drastically. More specifically, as seeking multimedia with emotional access points has been popular, the needs for indexing in terms of abstract concepts including emotions have grown. This study aims to analyze the index terms extracted from Getty Image Bank. Five basic emotion terms, which are sadness, love, horror, happiness, anger, were used when collected the indexing terms. A total 22,675 index terms were used for this study. The data are three sets; entire emotion, positive emotion, and negative emotion. For these three data sets, co-word occurrence matrices were created and visualized in weighted network with PNNC clusters. The entire emotion network demonstrates three clusters and 20 sub-clusters. On the other hand, positive emotion network and negative emotion network show 10 clusters, respectively. The results point out three elements for next generation of multimedia retrieval: (1) the analysis on index terms for emotions shown in people on image, (2) the relationship between connotative term and denotative term and possibility for inferring connotative terms from denotative terms using the relationship, and (3) the significance of thesaurus on connotative term in order to expand related terms or synonyms for better access points.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2013.07a
• /
• pp.31-32
• /
• 2013

본 논문에서는 영화의 등장인물의 성향을 파악하기 위해 시나리오의 대사로부터 감정어를 추출하고, 등장인물의 감정어들을 긍정, 부정, 중립의 3개로 단순화하여 등장인물의 성향을 가시화 시켜주는 방법을 제안한다. 대사로부터 감정어를 추출하기 위해 WordNet 기반의 감정어 추출 방법을 제안한다. WordNet은 단어 간에 상위어와 하위어, 유사어 등의 관계로 연결된 네트워크 구조의 사전이다. 이 네트워크 구조에서 최상위의 감정 항목과의 거리를 계산하여 단어별 감정량을 계산하여 대사를 30 차원의 감정 벡터로 표현한다. 등장인물별로 추출된 감정 벡터를 긍정, 부정, 중립의 3개의 차원으로 단순화 하여 등장인물의 성향을 표현한다.

• The Journal of the Korea Contents Association
• /
• v.11 no.5
• /
• pp.413-423
• /
• 2011

The objective of this study was to explain structural relations among perceived justice of service recovery, customer's emotion and satisfaction in the airline service sector. For this purpose, using a sample of 272 airline service customers who experienced complaints with airline service, we investigate the effects of the dimensions of perceived justice on the emotions triggered by service recovery. In this study, 4 hypotheses based on literature reviews were employed. The data and hypotheses were examined using Structural Equation Modeling(SEM) by AMOS. The main findings are as follows. Firstly, procedural justice has an effect on both positive and negative emotions. Secondly, both interaction justice and distributive justice have effects on positive emotion but not on negative emotion. Lastly, only positive emotion has influence on recovery satisfaction.

• Science of Emotion and Sensibility
• /
• v.26 no.3
• /
• pp.83-100
• /
• 2023

This study determines whether the perception of the frequency of experiencing positive, negative, and surprise emotions changes according to the level of cognitive well-being. Furthermore, we determined practical means to analyze which emotions can be managed in daily life as an effective means of improving overall life satisfaction by identifying representative specific emotions that strongly predict the level of cognitive well-being. To this end, the between-subjects factorial design is adopted to measure the frequency of emotional experiences according to the level of cognitive well-being in 438 university undergraduate students. For cognitive well-being, the life satisfaction scale (SWLS) was used, and the PANAS-X scale was used to measure emotional frequency. As a result, first, the group with high cognitive well-being displays a higher frequency of positive and surprise emotional experiences and a lower frequency of negative emotional experiences than the group with low cognitive well-being. Second, the results confirm that representative emotions affecting cognitive well-being included 8 positive emotions, 7 negative emotions, and 1 surprise emotion. Among them, positive emotions include "happy" and "confident," negative emotions include "dissatisfied with self" and "disgusted with self," and surprise emotions include words such as "amazed." Therefore, we can conclude that the representative emotions are those with the greatest influence on cognitive well-being. Therefore, increasing the frequency of specific emotions (e.g., happy, confident, and surprise) and decreasing the frequency of others (e.g., dissatisfied with self and disgusted with self) could be effective in improving cognitive well-being than unconditionally examining emotions experienced in daily life.

• Annual Conference on Human and Language Technology
• /
• 2021.10a
• /
• pp.379-383
• /
• 2021

감정 분석은 텍스트 데이터에서 인간이 느끼는 감정을 다양한 감정 유형으로 분류하는 것이다. 그러나 많은 연구에서 감정 분석은 긍정과 부정, 또는 중립의 극성을 분류하는 감성 분석의 개념과 혼용되고 있다. 본 연구에서는 텍스트에서 느껴지는 감정들을 다양한 감정 유형으로 분류한 감정 말뭉치를 구축하였는데, 감정 말뭉치를 구축하기 위해 심리학 모델을 기반으로 분류한 감정 어휘 사전을 사용하였다. 9가지 감정 유형으로 분류된 한국어 감정 어휘 사전을 바탕으로 한국어 영화 리뷰 말뭉치에 9가지 감정 유형의 감정을 태깅하여 감정 분석 말뭉치를 구축하고, KcBert에 학습시켰다. 긍정과 부정으로 분류된 데이터로 사전 학습된 KcBert에 9개의 유형으로 분류된 데이터를 학습시켜 기존 모델과 성능 비교를 한 결과, KcBert는 다중 분류 모델에서도 우수한 성능을 보였다.

• The Journal of Society for e-Business Studies
• /
• v.21 no.3
• /
• pp.15-28
• /
• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• The Journal of the Korea Contents Association
• /
• v.10 no.9
• /
• pp.432-437
• /
• 2010

The purpose of this research is to find out how festival servicescape and human service impact the participant's emotional response and behavioral intention in terms of participants as consumers. The servicescape of festival indicated 4 common factors, named cleanliness, convenience, suitability and attravtiveness. As a result of analysis, it indicated that cleanliness and convenience from among the servicescape of festival variables are positively on positive emotion, negatively on negative emotion. And it was analyzed that all the variables of human service influence significantly on emotional response. Especially, it showed that expertise of human service variables influence on positive emotion the most, influence significantly on negative emotion. The result of relationship on behavioral intention indicated that the convenience factor of festival servicescape and expertise factor of human service influence significantly. Therefore, to operate successful festival, festival organizer should plan and manage the festival based on factors which comprises festival servicescape and human service.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2009.05a
• /
• pp.113-116
• /
• 2009

보다 바람직한 운전 경험을 제공하기 위하여, 운전자의 감성에 영향을 미치는 요인에 대한 관심이 높아져 가고 있다. 본 연구에서는 운전자의 운전 시의 기분 혹은 감정상태에 영향을 미치는 외부요인으로 도로 소통 상황, 계절, 날씨, 운전 시각, 운전 지역을 추출하였으며 운전자 개인의 감정상태를 결정짓는 주관적이고 심리적인 요인은 배제하였다. 운전 경력 3 년 이상의 운전자를 대상으로 감성 상태는 긍정/부정, 흥분/안정의 2 가지 축으로 나뉘어 측정하였다. 운전자의 무드는 중립을 기준으로 양 극단보다는 중립에 가까운 상태에 주로 분포하였는데 긍정/부정에는 각각 고루 분포한 반면, 흥분/안정에서는 안정 쪽에 더 많이 분포한 것을 알 수 있었다. 또한 다섯 가지 외부 요인들이 모두 운전자의 무드에 영향을 미쳤으며 도로 소통 상황이 긍정/부정과 흥분/안정 모두에서 유의미한 차이를 보였다. 감성의 긍정/부정 축에서는 계절, 날씨, 소통 상황, 그리고 운전 지역이 유의미하였으며, 감성의 흥분/안정 축에서는 운전시간과 소통 상황이 유의미하였다. 본 연구의 결과를 확장시켜 운전자의 감성에 영향을 미치는 외부 요인들을 데이터베이스화하여 운전자를 위한 다양한 제품 및 서비스 개발에 활용될 수 있을 것으로 기대한다.

• Annual Conference on Human and Language Technology
• /
• 2015.10a
• /
• pp.147-150
• /
• 2015

감성은 개인적인 생활경험을 통해 표현되며 동일한 감정상태와 정보자극을 주더라도 다른 감성이 발생될 뿐만 아니라 개인, 사회, 문화 요인에 따라서 크게 변한다. 따라서 다른 영역의 감성과 도서에 대한 감성이 같지 않기 때문에 별도의 감성 사전 구축이 필요하다. 구축된 감성사전은 비슷한 성향의 도서와 사람을 묶어 추천해 주는데 활용할 수 있다. 감성 사전 구축을 위한 원천 정보로 네티즌이 책을 읽고 호감도와 함께 짧은 문장으로 쓴 소감을 활용하였다. 감성분석에서 가장 기본이 되는 분류는 긍정과 부정으로 나누는 것이다. 하지만, 실제로 도서를 추천하기위해서 긍정과 부정으로만 구분하는 것은 충분하지 않다. 따라서 본 연구에서는 도서에 대해서 감성을 긍정과 부정의 호감정도와 감성의 활성도를 조합한 8개의 감성으로 분류하고 각각의 지수를 함께 산출하여 감성어 사전을 구축하고 활용하는 방안을 제시하였다.