• Title, Summary, Keyword: Emotion Dictionary

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Classification and Intensity Assessment of Korean Emotion Expressing Idioms for Human Emotion Recognition

  • Park, Ji-Eun;Sohn, Sun-Ju;Sohn, Jin-Hun
    • Journal of the Ergonomics Society of Korea
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    • v.31 no.5
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    • pp.617-627
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    • 2012
  • Objective: The aim of the study was to develop a most widely used Korean dictionary of emotion expressing idioms. This is anticipated to assist the development of software technology that recognizes and responds to verbally expressed human emotions. Method: Through rigorous and strategic classification processes, idiomatic expressions included in this dictionary have been rated in terms of nine different emotions (i.e., happiness, sadness, fear, anger, surprise, disgust, interest, boredom, and pain) for meaning and intensity associated with each expression. Result: The Korean dictionary of emotion expression idioms included 427 expressions, with approximately two thirds classified as 'happiness'(n=96), 'sadness'(n=96), and 'anger'(n=90) emotions. Conclusion: The significance of this study primarily rests in the development of a practical language tool that contains Korean idiomatic expressions of emotions, provision of information on meaning and strength, and identification of idioms connoting two or more emotions. Application: Study findings can be utilized in emotion recognition research, particularly in identifying primary and secondary emotions as well as understanding intensity associated with various idioms used in emotion expressions. In clinical settings, information provided from this research may also enhance helping professionals' competence in verbally communicating patients' emotional needs.

The Construction of a Domain-Specific Sentiment Dictionary Using Graph-based Semi-supervised Learning Method (그래프 기반 준지도 학습 방법을 이용한 특정분야 감성사전 구축)

  • Kim, Jung-Ho;Oh, Yean-Ju;Chae, Soo-Hoan
    • Science of Emotion and Sensibility
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    • v.18 no.1
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    • pp.103-110
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    • 2015
  • Sentiment lexicon is an essential element for expressing sentiment on a text or recognizing sentiment from a text. We propose a graph-based semi-supervised learning method to construct a sentiment dictionary as sentiment lexicon set. In particular, we focus on the construction of domain-specific sentiment dictionary. The proposed method makes up a graph according to lexicons and proximity among lexicons, and sentiments of some lexicons which already know their sentiment values are propagated throughout all of the lexicons on the graph. There are two typical types of the sentiment lexicon, sentiment words and sentiment phrase, and we construct a sentiment dictionary by creating each graph of them and infer sentiment of all sentiment lexicons. In order to verify our proposed method, we constructed a sentiment dictionary specific to the movie domain, and conducted sentiment classification experiments with it. As a result, it have been shown that the classification performance using the sentiment dictionary is better than the other using typical general-purpose sentiment dictionary.

Emotion Analysis System for Social Media using Sentiment Dictionary including newly created word (신조어 감성사전 기반의 소셜미디어 감성분석 시스템)

  • Shin, Panseop;Oh, Hanmin
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.225-226
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    • 2019
  • 오피니언 마이닝은 온라인 문서의 감성을 추출하여 분석하는 기법이다. 별도의 여론조사 없이 감성을 분석 가능하므로, 최근 활발한 연구 분야이다. 그러나 소셜미디어에는 신조어 등이 많이 포함되어 있어 기존 감성분석 시스템으로는 정확한 분석이 어려울 뿐만 아니라, 복합적인 감성에 대한 분석을 내리기에 불리하다. 이에 본 연구에서는 직관적인 감성모델을 제안하고 SNS에서 주목받는 다양한 신조어를 수용한 감성단어사전을 구축한 후, 이를 적용하여 소셜미디어에 나타나는 복합적인 감성을 분석하는 감성분석시스템을 설계한다.

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Emotional analysis system for social media using sentiment dictionary with newly-created words

  • Shin, Pan-Seop
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.4
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    • pp.133-140
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    • 2020
  • Emotional analysis is an application of opinion mining that analyzes opinions and tendencies of people appearing in unstructured text. Recently, emotional analysis of social media has attracted attention, but social media contains newly-created words and slang, so it is not easy to analyze with existing emotional analysis. In this study, I design a new emotional analysis system to solve these problems. The proposed system is possible to analyze various emotions as well as positive and negative in social media including newly-created words and slang. First, I collect newly-created words and slang related to emotions that appear in social media. Then, expand the existing emotional model and use it to quantify the degree of sentiment in emotional words. Also, a new sentiment dictionary is constructed by reflecting the degree of sentiment. Finally, I design an emotional analysis system that applies an sentiment dictionary that includes newly-created words and an extended emotional model.

AN ALGORITHM FOR CLASSIFYING EMOTION OF SENTENCES AND A METHOD TO DIVIDE A TEXT INTO SOME SCENES BASED ON THE EMOTION OF SENTENCES

  • Fukoshi, Hirotaka;Sugimoto, Futoshi;Yoneyama, Masahide
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.773-777
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    • 2009
  • In recent years, the field of synthesizing voice has been developed rapidly, and the technologies such as reading aloud an email or sound guidance of a car navigation system are used in various scenes of our life. The sound quality is monotonous like reading news. It is preferable for a text such as a novel to be read by the voice that expresses emotions wealthily. Therefore, we have been trying to develop a system reading aloud novels automatically that are expressed clear emotions comparatively such as juvenile literature. At first it is necessary to identify emotions expressed in a sentence in texts in order to make a computer read texts with an emotionally expressive voice. A method on the basis of the meaning interpretation that utilized artificial intelligence technology for a method to specify emotions of texts is thought, but it is very difficult with the current technology. Therefore, we propose a method to determine only emotion every sentence in a novel by a simpler way. This method determines the emotion of a sentence according to an emotion that words such as a verb in a Japanese verb sentence, and an adjective and an adverb in a adjective sentence, have. The emotional characteristics that these words have are prepared beforehand as a emotional words dictionary by us. The emotions used here are seven types: "joy," "sorrow," "anger," "surprise," "terror," "aversion" or "neutral."

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Development of Open Dictionary for Emotional Communication (감성 커뮤니케이션을 위한 오픈사전 앱 개발)

  • Jo, Hun Gi;Lee, Geon Hun;Choi, Yeong Wan;Kim, Ji Sim;Kim, Kyong Ah;Ahn, You Jung
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.289-290
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    • 2019
  • 사람들의 대화에 사용되는 단어는 사전적으로 정의되어 있으나 개인마다 단어의 의미를 다르게 받아들이기도 한다. 단어에는 단순히 사전의 정의만 담겨 있는 것이 아니라 개인의 감정과 삶의 시간 또한 담겨 있기 때문이다. 각기 다른 의미로 단어를 사용함에 따라 야기되는 소통 장애를 해소하기 위하여 본 연구에서는 단어의 일반적 정의뿐 아니라 개개인 각자의 의미를 담을 수 있는 오픈사전 앱을 개발하였다. 일반사전에서는 공공 API를 통해 국립국어원의 우리말샘 사전을 사용하며, 오픈사전에서는 웹서버를 연동하여 MySQL에 사용자가 정의한 단어를 등록한다. 또한 본 앱에서는 해상도별 이미지를 구현하여 다양한 화면에 대응하였다.

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A study on unstructured text mining algorithm through R programming based on data dictionary (Data Dictionary 기반의 R Programming을 통한 비정형 Text Mining Algorithm 연구)

  • Lee, Jong Hwa;Lee, Hyun-Kyu
    • Journal of the Korea Industrial Information Systems Research
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    • v.20 no.2
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    • pp.113-124
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    • 2015
  • Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

A study about the aspect of translation on 'Kyo(驚)' in novel 『Kokoro』 -Focusing on novels translated in Korean and English (소설 『こころ』에 나타난 감정표현 '경(驚)'에 관한 번역 양상 - 한국어 번역 작품과 영어 번역 작품을 중심으로 -)

  • Yang, JungSoon
    • 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.

A study about the aspect of translation on 'Hu(怖)' in novel 『Kokoro』 - Focusing on novels translated in Korean and English - (소설 『こころ』에 나타난 감정표현 '포(怖)'에 관한 번역 양상 - 한국어 번역 작품과 영어 번역 작품을 중심으로 -)

  • Yang, Jung-soon
    • Cross-Cultural Studies
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    • v.53
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    • pp.131-161
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    • 2018
  • Emotional expressions are expressions that show the internal condition of mind or consciousness. Types of emotional expressions include vocabulary that describes emotion, the composition of sentences that expresses emotion such as an exclamatory sentence and rhetorical question, expressions of interjection, appellation, causative, passive, adverbs of attitude for an idea, and a style of writing. This study focuses on vocabulary that describes emotion and analyzes the aspect of translation when emotional expressions of 'Hu(怖)' is shown on "Kokoro". The aspect of translation was analyzed by three categories as follows; a part of speech, handling of subjects, and classification of meanings. As a result, the aspect of translation for expressions of Hu(怖)' showed that they were translated to vocabulary as they were suggested in the dictionary in some cases. However, they were not always translated as they were suggested in the dictionary. Vocabulary that described the emotion of 'Hu(怖)' in Japanese sentences were mostly translated to their corresponding parts of speech in Korean. Some adverbs needed to add 'verbs' when they were translated. Also, different vocabulary was added or used to maximize emotion. However, the correspondence of a part of speech in English was different from Korean. Examples of Japanese sentences that expressed 'Hu(怖)' by verbs were translated to expression of participles for passive verbs such as 'fear', 'dread', 'worry', and 'terrify' in many cases. Also, idioms were translated with focus on the function of sentences rather than the form of sentences. Examples, what was expressed in adverbs did not accompany verbs of 'Hu (怖)'. Instead, it was translated to the expression of participles for passive verbs and adjectives such as 'dread', 'worry', and 'terrify' in many cases. The main agents of emotion were shown in the first person and the third person in simple sentences. The translation on emotional expressions when a main agent was the first person showed that the fundamental word order of Japanese was translated as it was in Korean. However, adverbs of time and adverbs of degree tended to be added. Also, 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 the cause of events were positioned at the place of subject in some cases to show the degree of 'Hu(怖)' 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 a 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 humans who were the main agents of emotion. They could be things or causes of events that specified the expression of emotion.

Emotion Recognition from Natural Language Text Using Predicate Logic Form (Predicate Logic Form을 이용한 자연어 텍스트로부터의 감정인식)

  • Seol, Yong-Soo;Kim, Dong-Joo;Kim, Han-Woo;Park, Jung-Ki
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.411-412
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    • 2010
  • 전통적으로 자연어 텍스트로부터의 감정인식 연구는 감정 키워드에 기반한다. 그러나 감정 키워드만을 이용하면 자연어 문장이 원래 갖고 있는 통사정보나 의미정보는 잃어버리게 된다. 이를 극복하기 위해 본 논문에서는 자연어 텍스트를 Predicate Logic 형태로 변환하여 감정 정보처리의 기반데이터로 사용한다. Predicate Logic형태로 변환하기 위해서 의존 문법 구문분석기를 사용하였다. 이렇게 생성된 Predicate 데이터 중 감정 정보를 갖고 있는 Predicate만을 찾아내는데 이를 위해 Emotional Predicate Dictionary를 구축하였고 이 사전에는 하나의 Predicate마다 미리 정의된 개념 클래스로 사상 시킬 수 있는 정보를 갖고 있다. 개념 클래스는 감정정보를 갖고 있는지, 어떤 감정인지, 어떤 상황에서 발생하는 감정인지에 대한 정보를 나타낸다. 자연어 텍스트가 Predicate으로 변환되고 다시 개념 클래스로 사상되고 나면 KBANN으로 구현된 Lazarus의 감정 생성 규칙에 적용시켜 최종적으로 인식된 감정을 판단한다. 실험을 통해 구현된 시스템이 인간이 인식한 감정과 약 70%이상 유사한 인식 결과를 나타냄을 보인다.

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