• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.157-175
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• 2010

Understanding dialogue participant's emotion is important as well as decoding the explicit message in human communication. It is well known that non-verbal elements are more suitable for conveying speaker's emotions than verbal elements. Written texts, however, contain a variety of linguistic units that express emotions. This study aims at analyzing components for constructing an emotion ontology, that provides us with numerous applications in Human Language Technology. A majority of the previous work in text-based emotion processing focused on the classification of emotions, the construction of a dictionary describing emotion, and the retrieval of those lexica in texts through keyword spotting and/or syntactic parsing techniques. The retrieved or computed emotions based on that process did not show good results in terms of accuracy. Thus, more sophisticate components analysis is proposed and the linguistic factors are introduced in this study. (1) 5 linguistic types of emotion expressions are differentiated in terms of target (verbal/non-verbal) and the method (expressive/descriptive/iconic). The correlations among them as well as their correlation with the non-verbal expressive type are also determined. This characteristic is expected to guarantees more adaptability to our ontology in multi-modal environments. (2) As emotion-related components, this study proposes 24 emotion types, the 5-scale intensity (-2~+2), and the 3-scale polarity (positive/negative/neutral) which can describe a variety of emotions in more detail and in standardized way. (3) We introduce verbal expression-related components, such as 'experiencer', 'description target', 'description method' and 'linguistic features', which can classify and tag appropriately verbal expressions of emotions. (4) Adopting the linguistic tag sets proposed by ISO and TEI and providing the mapping table between our classification of emotions and Plutchik's, our ontology can be easily employed for multilingual processing.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.133-141
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• 2016

Recent web services provide tagging function to users, and let them express the topic of the contents of their articles. Moreover, we can extract context information like emotion of the writer efficiently by using tags attached to the articles or images. And we are able to better understand article than traditional algorithm. (eg. TF-IDF) Therefore, if we use tags in recommendation system, we can recommend high quality resources to the users. This study proposes a recommendation method that provide web resources (articles, users) through simple algorithm based on related tag set extracted from the article. Through the experiments, we show that the result was satisfactory, and we measure the satisfaction of users.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.214-220
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• 2021

With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.259-262
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• 2011

최근 들어 소리의 세기나 하모니, 템포, 리듬 등의 다양한 음악 신호 특성을 기반으로 한 음악 무드 분류에 대한 연구가 활발하게 진행되고 있다. 본 논문에서는 음악 무드 분류의 정확도를 높이기 위하여 음악 신호 특성과 더불어 노래 가사와 소셜 네트워크 상에서의 사용자 평가 등을 함께 고려하는 멀티 모달 음악 무드 분류 기법을 제안한다. 이를 위해, 우선 음악 신호 특성에 대해 퍼지 추론 기반의 음악 무드 추출 기법을 적용하여 다수의 가능한 음악 무드를 추출한다. 다음으로 음악 가사에 대해 TF-IDF 기법을 적용하여 대표 감정 키워드를 추출하고 학습시킨 가사 무드 분류기를 사용하여 가사 음악 무드를 추출한다. 마지막으로 소셜 네트워크 상에서의 사용자 태그 등 사용자 피드백을 통한 음악 무드를 추출한다. 특정 음악에 대해 이러한 다양한 경로를 통한 음악 무드를 교차 분석하여 최종적으로 음악 무드를 결정한다. 음악 분류를 기반한 자동 음악 추천을 수행하는 사용자 만족도 평가 실험을 통해서 제안하는 기법의 효율성을 검증한다.

• Annual Conference of KIPS
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• 2016.10a
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• pp.640-643
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• 2016

오피니언 마이닝은 객관적인 정보를 필요로 하는 많은 분야에서 쓰이는 기법이다. 그러나 표현의 자유도가 높은 한글 Text를 분석하는 것은 상당히 어려운 일이다. 또한 한글 파괴 현상도 하나의 원인으로 대두되고 있다. 본 논문에서는 Text를 음소단위로 분할하는 Trigrarn-Signature 기법과 구문태그 패턴 기법을 통합한 새로운 상태 분류 기법을 제안했고, 만족, 불만, 낙담, 의문, 흥분 5가지 감정 분류를 시도했다. 이를 토대로 사용자의 정보를 그래프로 보여주는 시각화 시스템을 제안한다.

• 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.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.9-17
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• 2010

Nowadays, many research have been conducted on robots or interactive characters that properly respond to the users affection. In this paper, we develop an intelligent messenger that provides appropriate responses to text inputs according to user's intention and affection. In order to properly respond, the intelligent messenger adapts methods to recognize user's speech act and affection. And it uses an AIML-based interactive script to which tags are additionally attached to express affection and speech act. If the intelligent messenger finds a proper reply in the interactive scripts, it displays the reply in a dialog window, and an animation character expresses emotion assimilated with a user's affection. If the animation character is synchronized with a content robot through a wireless link, the robot in the same space with the user can provide emotional response.

• Journal of Korea Multimedia Society
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• v.18 no.11
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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 Information Systems
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• v.27 no.2
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• pp.77-94
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• 2018

Purpose As the amount of data became enormous, it became a time when more efforts were needed to find the necessary information. Curation is a new term similarly to the museum curator, which is a service that helps people to collect, share, and value the contents of the Internet. In SNS, hash tag is used for emotional vocabulary to be transmitted between users by using (#) tag. Design/methodology/approach As the amount of data became enormous, it became a time when more efforts were needed to find the necessary information. Curation is a new term similarly to the museum curator, which is a service that helps people to collect, share, and value the contents of the Internet. In SNS, hash tag is used for emotional vocabulary to be transmitted between users by using (#) tag. Findings This study base on seven emotional sets such as 'Happy', 'Angry', 'Sad', 'Bad', 'Fearful', 'Surprised', 'Disgusted' to construct 327 emotional seeds and utilize the autofill function of web browser to collect 1.5 million emotional words from emotional seeds. The emotional dictionary of this study is considered to be meaningful as a tool to make emotional judgment from unstructured data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.147-149
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• 2021

Recently, the rapid growth of the media market and the expectations of users have been increasing. In this research, tags are extracted through media-derived audio and classified into specific categories using artificial intelligence. This category is a type of emotion including joy, anger, sadness, love, hatred, desire, etc. We use JupyterNotebook to conduct the corresponding study, analyze voice data using the LiBROSA library within JupyterNotebook, and use Neural Network using keras and layer models.