• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.142-151
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• 2023

Sentiment analysis is a method used to comprehend feelings, opinions, and attitudes in text, and it is essential for evaluating consumer feedback and social media posts. However, creating sentiment dictionaries, which are necessary for this analysis, is complex and time-consuming because people express their emotions differently depending on the context and domain. In this study, we propose a new method for simplifying this procedure. We utilize syntax analysis of the Korean language to identify and extract sentiment words based on the Reason-Sentiment Pattern, which distinguishes between words expressing feelings and words explaining why those feelings are expressed, making it applicable in various contexts and domains. We also define sentiment words as those with clear polarity, even when used independently and exclude words whose polarity varies with context and domain. This approach enables the extraction of explicit sentiment expressions, enhancing the accuracy of sentiment analysis at the attribute level. Our methodology, validated using Korean cosmetics review datasets from Korean online shopping malls, demonstrates how a sentiment dictionary focused solely on clear polarity words can provide valuable insights for product planners. Understanding the polarity and reasons behind specific attributes enables improvement of product weaknesses and emphasis on strengths. This approach not only reduces dependency on extensive sentiment dictionaries but also offers high accuracy and applicability across various domains.

• Journal of the Korean Society for information Management
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• v.17 no.4
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• pp.27-45
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• 2000

National statistical data such as Korean Census is fundamental data for national administration. In this paper, we present an automatic coding system utilizing morphological analyser and knowledge dictionaries. Knowledge bases are constructed based on an authority dictionaries which were developed by authors utilizing a newly learning theory. Test data indicates 99.5% of productivity and 83.3% of accuracy. The presented methods can be effectively applied to analyze statistical information.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.21-29
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• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.1-21
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• 2016

IT-based subjective well-being (SWB) services, a main part of wellness IT, should measure the SWB state of individuals in an unrestrained, cost-effective manner. The dictionaries for sentiment analysis available in the market may be useful for this purpose, but obtaining proper sentiment values using only words from the sentiment lexicon is impossible; therefore, a new dictionary including wellness vocabulary is needed. The existing sentiment dictionaries link only a single sentiment value to a single sentiment word, although sentiment values may vary depending on personal traits. In this study, we develop an extended version of the SenticNet sentiment dictionary dubbed WellnessWordNet. SenticNet is considered the best and most expressive among the already existing sentiment dictionaries. Using the information provided by SenticNet, we created a database including the wellness states (estimated values) of stress, depression, and anger to develop the WellnessWordNet system. The accuracy of the system was validated through actual tests with live subjects. This study is unique and unprecedented in that i) an extended sentiment dictionary, WellnessWordNet, is developed; ii) values for wellness state language are offered; and iii) different sentiment values, namely contextual polarity, for people of the same gender or age group are suggested.

• Korean Journal of Child Studies
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• v.5
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• pp.47-52
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• 1984

인지 어휘 유형(cognitive dictionary)이란 단어 연상의 반응 어휘를 인지 유형에 따라 분류, 분석하는 것을 가리킨다. 인지 어휘 유형 개념을 McNeill의 언어 발달 연구에 준하여 논의하였다. 즉 아동의 어의(語義) 발달은 자작문(自作文) 형식(形式) 표현에서 시작되어 어휘 사용에 이른다는 것이다. 한편 Moran은 범세계적으로 유아들의 인지 어휘 유형은 단어의 동작적(動作的) 특성에 주로 의거한다는 것을 발견했는데 이는 언어의 효시에 관한 Piaget 나 Bruner의 이론과 일치하는 것이다. Moran의 인지 어휘 유형의 추가 개념은 Bruner의 심상(心象)(ikonic representation)에 의한 관계, 기능적 관계 (functional representation), 논리적(logical)관계를 포함한 단어의 연합 관계에 반영시켰다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.314-316
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• 2016

본 연구에서는 IHO에서 발표한 S-100 표준에 맞춰 Feature Catalogue Builder의 고도화와 S-100 표준 등록소를 개발하였다. 기존의 S-57 기반에서는 Object, Attribute와 도메인의 효율적인 관리가 어려웠으나, S-100 기반의 시스템에서는 새로운 Item의 제안과 검토, 승인 절차 등이 표준화가 되어있어 보다 효과적인 관리가 가능할 것으로 예측된다.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.655-658
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• 1999

In this paper we design a key-frame editor for 3D sign-language animation using the inverse kinematics. Using the editor, we can calculate the joint angles for two arms automatically. Up to now we have computed the values of the joint angles using the forward kinematics, where we have determined the values heuristically based on our experiences. To overcome the drawbacks, we employ the arm transformation matrix of the inverse kinematics. Experimental results show a possibility that the proposed method could be used for making up the sign-language communication dictionaries.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.177-189
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• 2001

ㆍ RosettaNet is one of the most rapidly expanding XML- based B2B standards aiming at lingua-franca for e-Business. ㆍ RosettaNet delivers PIP/sup TM/-based B2B process standards, dictionaries, and implementation frameworks guaranteeing interoperability among integration solutions. ㆍ RosettaNet is an evolutionary standard featuring need-based expansion, implementation-promoting development methodology, and release model based on collective agreement among members. ㆍ RosettaNet plans to continue to focus the majority of its efforts on vertical supply chain and business model specific e-commerce process standards, with an emphasis on rapid adoption and production implementation in the high technology industry.

• Korean Linguistics
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• v.76
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• pp.213-241
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• 2017

Korean verb 'Casi-', which is treated as negative element in dictionaries, is actually a placeholder shows indefinite use, and '-ko casiko' construction is understood as a member of a class of 'Ending repetition construction'. However, unlike among others, '-ko casiko' construction is now undergoing developement of its negative polarity. Because of its indefiniteness, '-ko casiko' construction change its meaning into a universal negative when it is used in negative sentences.

• Genomics & Informatics
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• v.17 no.2
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• pp.16.1-16.4
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• 2019

Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. Online investigation found that Japanese-English dictionaries, which assign MeSH information to applicable terms, but use them for NLP, were found to be difficult to access, due to license restrictions. Here, we investigate an open-source Japanese-English glossary as an alternative method for assigning MeSH IDs to Japanese terms, to obtain preliminary data for NLP proof-of-concept.