• Design Convergence Study
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• v.14 no.4
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• pp.1-16
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• 2015

This study aims to understand distribution of the sentimental words on each genre and find relationship between box office performance and sentimental words in movie review using 673 movies that have more than 1,000 reviews. For the analysis, crawling movie reviews and made data was composed movie genre, movie name, sales, attendance, screen, normal attendance, 7 sentimental words. For analysis results, we used correlation analysis and Parallel coordinates. As a results, First, the highest box office value of the genre is comedy and the lowest box office value of the genre is horror through analyze box office on each genre. Secondly, Movie genre of fantasy feel a lot of boring emotion and Movie genre of SF feel a lot of anger emotion even if 'Happy' and 'Surprise' have highest sentiment value on every genre. Third, We found 'Anger' increase sentimental value when 'Disgust' increase sentimental value and 'Surprise' decrease sentimental value when 'Happy' increase sentimental value through analyze correlation relationship between sentimental words using total data. Fourth, We found 'Happy' have linear relationship between box office and 'Fear' have non-linear relationship between box office through analyze sentimental words according to box office performance.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.519-521
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• 2019

최근 소셜 메신저를 통해 많은 사람들이 의사소통을 주고받음에 따라, 텍스트에서 감정을 파악하는 것이 중요하다. 따라서, 감정이 태깅된 데이터가 필요하다. 하지만, 기존 연구는 감정이 태깅된 데이터의 양이 많지가 않다. 이는 텍스트에서 감정을 파악하는데 성능 저하를 야기할 수 있다. 이를 해결하기 위해, 본 논문에서는 단어 매칭 방법과 형태소 매칭 방법을 이용하여 많은 양의 한국어 감정 태깅 데이터셋인 EmoNSMC 를 구축하였다. 구축한 데이터셋은 네이버 영화 감상 리뷰 데이터 (NSMC)에 디스턴트 수퍼비전 방법 (distant supervision) 방법을 적용하여 weak labeling을 진행하였고, 이 과정에서 한국어 감정 어휘 사전 (KTEA) 을 이용하였다. 구축된 데이터셋의 감정 분포 결과, 형태소 매칭 방법을 통해 구축한 데이터셋이 좀 더 감정 분포가 균등한 것을 확인할 수 있었다. 해당 데이터셋은 공개되어 있다.

• 한국HCI학회:학술대회논문집
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• 2007.02b
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• pp.48-54
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• 2007

컴퓨터 네트워크의 발달로 인터넷이 실생활이 되면서, 온라인에서 인스턴트 메신저를 이용한 커뮤니케이션 방식이 확대되고 있다. 이러한 메신저에서는 이모티콘 및 플래시콘 혹은 윙크 등 과 같은 다양한 방식을 통해 감정을 표현할 수 있도록 제안하고 있다. 하지만, 아직까지 온라인의 감정표현은 흥미위주이며, 오프라인의 다양한 감정표현에 비해서 제한적이다 따라서 오프라인에서의 다양한 사용자 감정표현을 온라인에서 표현해 줄 수 있는 새로운 형태의 감성 커뮤니케이션 방법이 필요하다. 본 연구에서는 온라인 인스턴트 메신저의 사용행태 이해를 통해서 사용자들의 요구사항을 파악하고 이를 통해 제안할 수 있는 커뮤니케이션 방법을 모색하고자 한다. 실생활에서 사용자의 감정표현 방법을 바탕으로 온라인 메신저에 적용하여 새로운 감성 커뮤니케이션 방법을 제안하는 연구로, 감정어휘와 표현방법의 사용자 조사를 통해 감정표현 방법에 대한 컨셉을 제안하였다. 우선 메신저 사용 행태와 요구사항 도출을 위한 사용자 FGI를 진행하였다. 사용자 FGI로 얻어진 사용자들의 메신저 사용행태를 통해, 텍스트와 모션을 활용하는 '텍스트콘'을 컨셉 아이디어로 도출하였다. 아이디어에 적용하기 위해 모션을 감정으로 인해 파생되는 행동과 체감각 요소로 구분하였다. 구체적인 표현방법 및 적용 요소를 추출하기 위한 방법으로 온라인 설문조사를 실시하였으며 감정어휘와 표현방법(행동, 체감각)을 추출하였다. 추출된 내용은 '사용자 참여 관찰' 기법을 활용하여, 사용어휘와 행동을 정리하고 구제화하여 그 결과를 '텍스트콘'에 적용하였다. 본 연구는 사용자들이 메신저를 이용한 감정표현에 있어 텍스트를 선호하고 텍스트에 대한 요구사형에 있음을 확인하였으며, 감정표현 컨셉 아이디어 '텍스트콘'을 도출하였다. '사용자 참여 관찰' 기법을 활용하여 기존의 이모티콘으로 대표되는 그래픽 아이콘 중심의 사용자 감정표현 방법을 보조해줄 수 있는 방법으로 실생활에서 사용자가 표현하고 받아들이는 신체언와 행동을 적용하였다는 것에 그 의의가 있다. 또한, 감정에 의한 생리적 반응 요소를 외적인 반응과 내적인 반응으로 구분하여, '체감각'이라고 하는 내적반응을 중심표현방법으로 삼아, 기존과는 다른 시각으로 접근할 수 있었다.

• Cross-Cultural Studies
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• v.51
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• pp.329-356
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• 2018

Types of emotional expressions are comprised of vocabulary that describes emotion and composition of sentences to express emotion such as an exclamatory sentence and a rhetorical question, expressions of interjection, adverbs of attitude for an idea, and a style of writing. This study is focused on vocabulary that describes emotion and analyzes the aspect of translation when emotional expression of 'Kyo(驚)' is shown in "Kokoro". As a result, the aspect of translation for expression of 'Kyo(驚)' showed that it was translated to vocabulary as suggested in the dictionary in some cases. However, it was not always translated as suggested in the dictionary. Vocabulary that describes the emotion of 'Kyo(驚)' in Japanese sentences is mostly translated to corresponding parts of speech in Korean. Some adverbs needed to add 'verbs' when they were translated. Different vocabulary was added or used to maximize emotion. However, the corresponding part of speech in English was different from Korean. Examples of Japanese sentences expressing 'Kyo(驚)' by verbs were translated to expression of participles for passive verbs such as 'surprise' 'astonish' 'amaze' 'shock' 'frighten' 'stun' in many cases. Idioms were also translated with focus on the function of sentences rather than the form of sentences. Those expressed in adverbs did not accompany verbs of 'Kyo(驚)'. They were translated to expression of participles for passive verbs and adjectives such as 'surprise' 'astonish' 'amaze' 'shock' 'frighten' 'stun' in many cases. Main agents of emotion were showat the first person and the third person in simple sentences. Translation of emotional expressions when a main agent was the first person showed that the fundamental word order of Japanese was translated as in Korean. However, adverbs of time and adverbs of degree were ended to be added. The first person as the main agent of emotion was positioned at the place of subject when it was translated in English. However, things or causes of events were positioned at the place of subject in some cases to show the degree of 'Kyo(驚)' which the main agent experienced. The expression of conjecture and supposition or a certain visual and auditory basis was added to translate the expression of emotion when the main agent of emotion was the third person. Simple sentences without the main agent of emotion showed that their subjects could be omitted even if they were essential components because they could be known through context in Korean. These omitted subjects were found and translated in English. Those subjects were not necessarily human who was the main agent of emotion. They could be things or causes of events that specified the expression of emotion.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.541-548
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• 2020

Interest in emotions continues to rise around the world, including Korea, and in recent years, new styles such as new-tro, a new term referring to 'gaem-seong' of the social networking service generation, have emerged. Considering the strong influence of emotion on time and the environment, it is necessary to recognize the public's emotion about the current new style. Thus, in this study, 12 emotional vocabulary words were selected from the nine products applied with the new-tro style: want, cute, happy, pleasure, unique, cool, stylish, pretty, fun, like, joy, and special. According to the analysis of the survey, men's emotions were higher in order of "funny-cute-happy," and women's emotions were higher in order of 40s, 30s, 50s and 20s. Emotional vocabulary extracted from this study can be used to evaluate the sensitivity of objects to which the new-tro style is applied and will be utilized as specific emotional evaluation data through the analysis methods presented.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.669-696
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• 2010

Assuming that the whole meaning of a document is a composition of the meanings of each part, this paper proposes to study the automatic grading of movie reviews which contain sentimental expressions. This will be accomplished by calculating the values of semantic segments and performing data classification for each review. The ARSSA(The Automatic Rating System for Sentiment analysis using an Appraisal dictionary) system is an effort to model decision making processes in a manner similar to that of the human mind. This aims to resolve the discontinuity between the numerical ranking and textual rationalization present in the binary structure of the current review rating system: {rate: review}. This model can be realized by performing analysis on the abstract menas extracted from each review. The performance of this system was experimentally calculated by performing a 10-fold Cross-Validation test of 1000 reviews obtained from the Naver Movie site. The system achieved an 85% F1 Score when compared to predefined values using a predefined appraisal dictionary.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.109-114
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• 2015

대화 시스템에서 사용자 발화에 대한 감정 분석은 적절한 시스템 응답과 서비스를 제공하는데 있어 매우 중요한 정보이다. 본 연구에서는 단순한 긍, 부정이 아닌 분노, 슬픔, 공포, 기쁨 등 Plutchick의 8 분류 체계에 해당하는 상세한 감정을 분석 하는 데 있어, 임베딩 모델을 사용하여 기존의 어휘 자질을 효과적으로 사용할 수 있는 새로운 방법을 제안한다. 또한 대화 속에서 발생한 감정의 지속성을 반영하기 위하여 문장 임베딩 벡터와 문맥 임베딩 벡터를 자질로서 이용하는 방법에 대해 제안한다. 실험 결과 제안하는 임베딩 자질은 특히 내용어에 대해 기존의 어휘 자질을 대체할 수 있으며, 데이터 부족 문제를 다소 해소하여 성능 향상에 도움이 되는 것으로 나타났다.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.105-120
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• 2012

This study aimed to develop a Korean emotion vocabulary list that functions as an important tool in understanding human feelings. In doing so, the focus was on the careful extraction of most widely used feeling words, as well as categorization into groups of emotion(s) in relation to its meaning when used in real life. A total of 12 professionals (including Korean major graduate students) partook in the study. Using the Korean 'word frequency list' developed by Yonsei University and through various sorting processes, the study condensed the original 64,666 emotion words into a finalized 504 words. In the next step, a total of 80 social work students evaluated and classified each word for its meaning and into any of the following categories that seem most appropriate for inclusion: 'happiness', 'sadness', 'fear', 'anger', 'disgust', 'surprise', 'interest', 'boredom', 'pain', 'neutral', and 'other'. Findings showed that, of the 504 feeling words, 426 words expressed a single emotion, whereas 72 words reflected two emotions (i.e., same word indicating two distinct emotions), and 6 words showing three emotions. Of the 426 words that represent a single emotion, 'sadness' was predominant, followed by 'anger' and 'happiness'. Amongst 72 words that showed two emotions were mostly a combination of 'anger' and 'disgust', followed by 'sadness' and 'fear', and 'happiness' and 'interest'. The significance of the study is on the development of a most adaptive list of Korean feeling words that can be meticulously combined with other emotion signals such as facial expression in optimizing emotion recognition research, particularly in the Human-Computer Interface (HCI) area. The identification of feeling words that connote more than one emotion is also noteworthy.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.487-490
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• 2004

이메일과 같은 텍스트 기반의 서비스 둥이 점차 대중화되고 있지만, 이러한 텍스트 기반의 서비스에서는 메시지를 전달할 때 수신자가 필자의 감정 상태를 정확하게 파악하기 어려운 문제가 있다. 이러한 문제를 단편적으로 해결하기 위하여 감정 상태를 나타내는 이모티콘(emoticon)을 사용하기도 하지만 이는 보편적이지 않아서 사용하기에 불편한 점이 있다. 따라서 본 논문에서는 이러한 문제를 해결하기 위한 방안으로 일반 텍스트 문서에 감정 태그를 삽입하여 필자의 감정을 표현할 수 있도록 새로운 마크업 언어인 EmoXML(Emotion XML)을 정의한다. 그리고 문장 내에 포함되어 있는 감정 어휘를 인식하여, 관련 감정 태그를 자동으로 생성하고 처리할 수 있는 시스템을 설계한다.