• Asia Marketing Journal
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• 제12권3호
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• pp.63-79
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• 2010

본 연구는 기업의 경제적 책임활동과 자선적 책임활동에 대한 소비자 인식이 반기업정서에 미치는 효과를 제시하였다. 본 연구결과에 의하면, 아이러니하게도 기업의 경제적 책임활동이 높을수록 반기업 정서가 오히려 더 높아지는 것으로 나타났다. 이러한 결과가 나타난 이유는 우리나라 대기업들이 경제적 분야에서 더 큰 기여를 하였다면, 소비자들이 해당 대기업들이 그만큼 비윤리적이고, 불법적인 방법으로 기업행위를 실시한다고 생각하기 때문으로 보인다. 또한, 기업에 대한 자선적 책임활동이 높게 지각될수록 반기업 정서에 부(negative)의 영향을 미치는 것으로 나타났다. 다시 말해서 기업의 자선적 책임활동이 높게 지각될수록 반기업 정서는 낮게 지각되었다. 끝으로 반기업 정서는 기업 평가에 부(negative)의 영향을 미치는 것으로 나타났다. 따라서 특정 기업에 대한 반기업 정서가 높을수록, 기업에 대한 태도는 낮게 평가되는 것을 알 수 있었다.

• 복식
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• 제21권
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• pp.101-112
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• 1993

In representing eroticism in fashion advertising the sentiments of the Post-Modernism thinking of only impulse and pleasure as practical and affirming a human life may he divided into the follwing expression First1 y, Sumbolic Expression The sentiments of Post-Modernism associ ates, the direction sexual intercourse by means of the state and action of wearing the costume or represents indirect symbols with the symbolic expression of an object or phenomenon Secondly, Masochistic, Sadistic Expression. The sentiments of the Post-Modernism in expressing eroticism give people masochistic or sadistic feelings or expressions by means of protographic technigues or the state or background of wearing the model's costume. Thirdly, Homosexual Expression. The sentiments of the Post-Modernism is appropriate to expressing an androgynous and unisex look to people as a role of meeting potential instincts. Fourthy, Feministic Expression. The sentiments of me Post-Modernism also express a subjective woman and represent an extension of her ego through the existence of the woman herself by means of the body-conscious look. Fifthly, Narcissistic Expression. The de-genre such as the introduction high technology and de-campus rnakes consumers have theirself-identity.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.189-200
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• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.712-720
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• 2022

The use of social media to disseminate news and interact with project stakeholders is increasing over time in the construction industry. Such social media data can be analyzed to get useful insights of the industry such as demands of new housing construction and satisfaction of construction workers. However, there has been a limited attempts to analyze social media data related to the construction industry. The objective of this study is to collect and analyze construction related tweets to understand the overall sentiments of individuals and organizations about the construction industry. The study collected 87,244 tweets from April 6, 2020, to April 13, 2020, which had hashtags relevant to the construction industry. The tweets were then analyzed to evaluate its sentiments polarity (positive or negative) and sentiment intensity or scores (-1 to +1). Descriptive statistics were produced for the tweets and the sentiment scores were visualized in a scatterplot to show the trend of the sentiment scores over time. The results shows that the overall sentiment score of all the tweets was slightly positive (0.0365). Negative tweets were retweeted and marked as favorite by more users on average than the positive ones. More specifically, the tweets with negative sentiments were retweeted by 2,802 users on average compared to the tweets with positive sentiments (247 average retweet count). This study can potentially be expanded in the future to produce a real time indicator of the construction market industry such as the increased availability of construction jobs, improved wage rates, and recession.

• 한국콘텐츠학회논문지
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• 제16권6호
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• pp.802-813
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• 2016

본 연구에서는 일본과 중국을 중심으로 한류의 공적인 담론 생산자이자 전달자인 언론인들이 인식하는 각국의 반한류 정서가 어느 수준으로 이해되며 어디에서 기인된 것인지, 그리고 그들이 인식하는 대안은 무엇인지 살펴보았다. 연구 결과, 한중일로 대변되는 동북아시아지역은 역사와 정치, 문화갈등에서 비롯된 반한 감정이 반한류에 영향을 미치는 양상을 보인다. 일본과 중국에서의 반한 감정은 지정학적 특수성과 역사적 관계가 문화 수용을 이해하는 기본 구조로 기능하며, 인터넷과 SNS 등을 통해 표출되고 전파되는 공통점을 보인다. 즉, 반한류 현상이 한류 콘텐츠에 대한 현지 수용자의 직접적인 반감이나 불만에서 비롯되지 않는다는 것이다. 하지만 양국 간의 차이도 감지되었다. 일본의 경우 반한류가 주로 '반한' 감정의 다른 표현에 불과한데 반해, 중국의 경우는 반한 감정이 실제로 한류 콘텐츠에 대한 불만을 촉발하거나, 반한류 감정으로 전환되거나, 접합되는 경향을 보인다는 점을 알 수 있었다.

• International Journal of Contents
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• 제15권4호
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• pp.65-73
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• 2019

Social media is a massive dataset in which individuals' thoughts are freely recorded. So there have been a variety of efforts to analyze it and to understand the social phenomenon. In this study, Twitter was used to define the moments when negative perceptions of the Korean Meteorological Administration (KMA) were displayed and the reasons people were dissatisfied with the KMA. Machine learning methods were used for sentiment analysis to automatically train the implied awareness on Twitter which mentioned the KMA July-October 2011-2014. The trained models were used to validate sentiments on Twitter 2015-2016, and the frequency of negative sentiments was compared with the satisfaction of forecast users. It was found that the frequency of the negative sentiments increased before satisfaction decreased sharply. And the tweet keywords and the news headlines were qualitatively compared to analyze the cause of negative sentiments. As a result, it was revealed that the individual caused the increase in the monthly negative sentiments increase in 2016. This study represents the value of sentiment analysis that can complement user satisfaction surveys. Also, combining Twitter and news headlines provided the idea of analyzing the causes of dissatisfaction that are difficult to identify with only satisfaction surveys. The results contribute to improving user satisfaction with weather services by efficiently managing changes in satisfaction.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• 지능정보연구
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• 제25권4호
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• pp.67-88
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• 2019

웹툰은 인터넷의 특징적 요소들을 활용하여 제작되는 만화 콘텐츠를 온라인 환경에서 소비 가능한 형태로 유통하는 한국형 디지털 만화 플랫폼이다. 최근 웹툰 산업의 급격한 성장과 함께 웹툰 콘텐츠의 공급량이 기하급수적으로 증가함에 따라, 효과적인 웹툰 콘텐츠 추천 방안의 필요성이 커지고 있다. 웹툰은 회화적 요소와 문학적 요소, 디지털 요소의 복합적 산물로서, 독자로 하여금 재미를 느끼게 하고 웹툰이 연출하는 상황에 이입·공감하게 하는 등 소비자의 감성을 자극하는 디지털 콘텐츠 상품이다. 따라서 웹툰이 소비자에게 전달하는 감성이 소비자가 웹툰을 선택함에 있어 중요한 기준으로 작용할 것이라 기대할 수 있다. 본 연구는 기존에 충분히 논의되지 않았던 소비자 감성을 중심으로, 웹툰 콘텐츠의 효과적인 추천을 지원할 수 있는 소비자 감성 패턴맵의 개발을 목적으로 한다. 본 연구의 수행을 위해 '네이버 웹툰' 플랫폼에서 서비스되는 200개 작품에 대한 메타데이터와 소비자 감성어휘 정보를 수집하였다. 분석 목적에 부합하지 않는 작품을 제외한 127개 작품에 대해 488개의 감성어휘가 수집되었다. 이후 수집된 감성어휘들 간 유사감성 통합, 중복감성 배제 과정을 Bottom-up 접근으로 수행하여 총 63개 감성유형으로 축소된 웹툰 특화 감성지표를 구축하였다. 구축한 감성지표에 대한 탐색적 요인분석을 수행하여 웹툰 유형을 분류할 수 있는 3개의 중요 차원을 도출하고, 이를 기준으로 K-Means 클러스터링을 수행하여 전체 웹툰을 4개 유형으로 분류하였다. 각각의 유형에 대해 웹툰-감성 2-Mode 네트워크를 구축하여 웹툰 유형별로 나타나는 감성 패턴의 특징을 살펴보았으며, 프로파일링 분석을 통해 웹툰 유형별 인사이트와 실무적으로 의미 있는 전략적 시사점을 도출할 수 있었다. 본 연구의 결과를 통해 웹툰의 추천 및 분류의 영역에서 소비자 감성의 활용 가능성을 확인하고, 웹툰 생태계 내 구성원들이 소비자를 보다 잘 이해하고 전략을 수립할 수 있도록 돕는 가이드라인을 제시하였다는 점에서 의의가 있다.