• Journal of the Korean Wood Science and Technology
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• 제51권6호
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• pp.458-469
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• 2023

Text mining techniques provide valuable insights into research information across various fields. In this study, text mining was used to identify research trends in wood science from 2012 to 2022, with a focus on representative journals published in Korea and Japan. Abstracts from Journal of the Korean Wood Science and Technology (JKWST, 785 articles) and Journal of Wood Science (JWS, 812 articles) obtained from the SCOPUS database were analyzed in terms of the word frequency (specifically, term frequency-inverse document frequency) and co-occurrence network analysis. Both journals showed a significant occurrence of words related to the physical and mechanical properties of wood. Furthermore, words related to wood species native to each country and their respective timber industries frequently appeared in both journals. CLT was a common keyword in engineering wood materials in Korea and Japan. In addition, the keywords "MDF," "MUF," and "GFRP" were ranked in the top 50 in Korea. Research on wood anatomy was inferred to be more active in Japan than in Korea. Co-occurrence network analysis showed that words related to the physical and structural characteristics of wood were organically related to wood materials.

• 대한음성학회지:말소리
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• 제60호
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• pp.29-45
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• 2006

We investigated whether the first syllable plays an important role in lexical access in Korean visual word recognition. To do so, one lexical decision task (LDT) and two form primed LDT experiments examined the nature of the syllabic neighborhood effect. In Experiment 1, the syllabic neighborhood density and the syllabic neighborhood frequency was manipulated. The results showed that lexical decision latencies were only influenced by the syllabic neighborhood frequency. The purpose of experiment 2 was to confirm the results of experiment 1 with form-primed LDT task. The lexical decision latency was slower in form-related condition compared to form-unrelated condition. The effect of syllabic neighborhood density was significant only in form-related condition. This means that the first syllable plays an important role in the sub-lexical process. In Experiment 3, we conducted another form-primed LDT task manipulating the number of syllabic neighbors in words with higher frequency neighborhood. The interaction of syllabic neighborhood density and form relation was significant. This result confirmed that the words with higher frequency neighborhood are more inhibited by neighbors sharing the first syllable than words with no higher frequency neighborhood in the lexical level. These findings suggest that the first syllable is the unit of neighborhood and the unit of representation in sub-lexical representation is syllable in Korea.

• 한국의상디자인학회지
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• 제25권1호
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• pp.83-96
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• 2023

The purpose of this study is to compare and analyze the perception of retro fashion and new-tro fashion using big data. TEXTOM allowed the collection of big data on the words 'retro fashion' and 'new-tro fashion', which was refined afterwards. As for the data collection period, Jan. 1, 2019 to Nov. 30, 2022 was set. A top 50 list of words were extracted from this data based on appearance frequency. The extracted words were processed through Network centrality analysis and CONCOR analysis using Ucinet 6. The results are as follows. 1) In retro fashion, the appearance frequency of 'style' was the highest, followed by 'sensibility', 'color', 'trend', 'fashion', and 'brand'. These words came up with high TF-IDF values. Network centrality analysis discovered that 'color', 'style', 'trend', 'sensibility', and 'design' had high level of connectivity with other words. CONCOR analysis showed a total of four significant groups; trends, styles, looks, and photos. 2) In new-tro fashion, the appearance frequency of 'retro' was the highest, followed by 'trend', 'generation', 'style', 'brand', and 'fashion'. These words also came up with high TF-IDF values. Network centrality analysis found that 'retro', 'trend', 'generation', and 'brand' had high level of connectivity with other words. CONCOR analysis showed a total of four significant groups; style, brand, clothing, and trend. 3) New-tro fashion is included in retro fashion in that it reproduces the styles of the past. However, it is taken completely differently from generation to generation. Unlike the older generations, millennials actively accept newly created clothes and brands based on the past styles. They perceive it as a fashion that reveals their own unique tastes and tastes.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2006년도 춘계 학술대회 발표논문집
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• pp.117-120
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• 2006

This paper examined two hypotheses. Firstly, if the first syllable of word play an important role in visual word recognition, it may be the unit of word neighbor. Secondly, if the first syllable is the unit of lexical access, the neighborhood size effect and the neighborhood frequency effect would appear in a lexical decision task and a form primed lexical decision task. We conducted two experiments. Experiment 1 showed that words had large neighbors made a inhibitory effect in the LDT(lexical decision task). Experiment 2 showed the interaction between the neighborhood frequency effectand the word form similarity in the form primed LDT. We concluded that the first syllable in Korean words might be the unit of word neighborhood and play a central role in a lexical access.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.325-326
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• 2019

본 논문은 텍스트 마이닝 기법으로 명사의 빈도수를 조사하여 워드클라우드를 나타내는 기존의 방법을 개선하여 지능적 워드클라우드를 구현하는 방법을 제안한다. 텍스트 마이닝 시에 명사 단어를 추출하는 사전에 누락된 신조어 등의 단어를 효과적으로 추가하고, 동사 등 다른 품사위주의 워드클라우드를 시각적으로 보여주는 방법을 제안한다. 실험에서 기존 명사의 빈도수 추출에는 KoNLP 패키지를 사용하였고, 지원되지 않는 신조어 80개를 추가하였고 빈도수를 수동으로 조사하여 추가하였다.

• 인지과학
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• 제20권3호
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• pp.309-333
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• 2009

2음절 한자 합성어의 어휘판단에서 형태소 처리와 글자 처리의 역할을 조사하였다. 실험 1의 단어에 대한 반응에서는 어두와 어말 위치 모두에서 형태소 빈도의 효과는 나타나지 않았으나, 비단어에 대한 반응에서는 글자 빈도의 효과와 글자-형태소 대응의 효과가 나타났다. 빈도가 높은 글자를 포함하는 비단어일수록 반응시간이 길었고, 글자-형태소의 대응이 불투명한 비단어일수록 반응시간이 길었다. 실험 2에서는 실험 1에서 나타난 글자-형태소 대응의 효과를 단어에서 직접 관찰하고자 하였다. 그 결과, 단어 자극에 대해서도 글자-형태소 대응이 불투명할수록 어휘 판단이 느렸으며, 비단어 자극에서 그러한 경향이 더 뚜렷하였다. 본 연구의 결과는, 글자-형태소 대응이 불투명한 단어의 경우 다양한 형태소를 활성화시키게 되고, 그 결과 형태소의 파악은 늦어지고, 결국은 단어 인지의 지연으로 연결된다는 주장을 지지한다. 실험 결과를 바탕으로 하여 한글 표기 한자어의 인지에서 형태소 위치 효과, 글자 빈도의 역할 등에 대해서 논의하였다.

• 응용통계연구
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• 제27권3호
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• pp.373-386
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• 2014

단어 구름은 문자 텍스트 상의 복수개의 단어들을 대상으로 그 단어들의 출현 빈도에 비례하는 글자의 크기나 글자의 색깔로 중요도를 나타내는 텍스트 시각화 방법이다. 이 그림은 텍스트 상의 핵심단어를 재빨리 인지하고 단어들의 상대적 출현빈도수에 맞추어 배열하는 데 유용하다. 동적 그래픽스를 이용하여 텍스트 장들의 변화에 따른 핵심단어와 단어출현빈도의 패턴의 변하는 모습을 살필 수 있다. 행들이 텍스트 상의 장들이고 열들이 텍스트에 출현하는 단어들의 출현빈도수 순위들인 단어출현빈도행렬을 정의할 수 있고 이 행렬을 이용하여 단어출현빈도행렬그림을 그릴 수 있다. 동적 그래픽스를 이용하여 출현빈도수 순위의 변화에 따른 단어출현빈도행렬의 패턴의 변하는 모습을 살필 수 있다. 우리는 단어 구름과 동적 그래픽스 기법을 사용하여 영어성경 텍스트 시각화를 수행할 수 있다.

• 한국병원경영학회지
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• 제28권2호
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• pp.32-43
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• 2023

Purposes: This research is conducted to identify main features and trends of mission, vision and core values in Korean tertiary general hospitals by using text-mining. Methodology: For the study, 45 mission, 112 vision and 190 core values are collected from 45 tertiary general hospitals' homepages in 2022 and use word frequency analysis and Leyword co-occurrence analysis. Findings: In the tertiary general hospitals' mission, there are high frequency words such as 'health', 'humanity', 'medical treatment', 'education', 'research', 'happiness', 'love', 'best', 'spirit', and mission mainly includes the content of contributing humanity's health and happiness with these words. In case of vision, high frequency words are 'hospital', 'medical treatment', 'research', 'lead', 'trust', 'centered', 'patient', 'best', 'future'. By using these words in vision, it represents the definition and characteristics of vision such as ideal organizations in the future, goals and targets. As a result of the Leyword co-occurrence analysis, vision includes the content of 'high-tech medical treatment', 'special care for patients', 'leading education and research', 'the highest trust with customer', 'creative talents training'. -astly, the high frequency word-pairs in core values are 'social distribution', 'innovation pursuit', 'cooperation and harmony', and it defines standards of behavior for organizations. Practical Implication: To correct the problems of vision, mission and core values from findings, firstly, it needs for Korean tertiary general hospitals to use the words that can explain organization's identity and differentiate others in their mission. Secondly, considering strengthening the role of hospitals in their community and the importance of members in organizations, it is necessary to establish vision with considering community and members to activate vision effectively. Thirdly, because there are no specific guidelines of establishing mission, vision and core values for healthcare organizations, this research concepts and results could be utilized when other organizations establish mission, vision and core values.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.211-223
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• 2018

To understand the meaning of data is a common goal of research on unstructured data. Among these unstructured data, there are difficulties in analyzing the meaning of unstructured data related to corpus and sentences. In the existing researches, the researchers used LSA to select sentences with the most similar meaning to specific words of the sentences. However, it is problematic to examine many sentences continuously. In order to solve unstructured data classification problem, several search sites are available to classify the frequency of words and to serve to users. In this paper, we propose a method of classifying documents by using the frequency of similar words, and the frequency of non-relevant words to be applied as weights, and storing them in terms of a multi-pattern storage. We use Tensorflow's Softmax to the nearby sentences for machine learning, and utilize it for unstructured data analysis and the inference of traffic information.

• 한국의류산업학회지
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• 제25권1호
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• pp.72-81
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• 2023

As changes in social and economic paradigms are accelerating, and non-contact has become the new normal due to the COVID-19 pandemic, metaverse services that build societies in online activities and virtual reality are spreading rapidly. This study analyzes the perception and trend of metaverse fashion using big data. TEXTOM was used to extract metaverse and fashion-related words from Naver and Google and analyze their frequency and importance. Additionally, structural equivalence analysis based on the derived main words was conducted to identify the perception and trend of metaverse fashion. The following results were obtained: First, term frequency(TF) analysis revealed the most frequently appearing words were "metaverse," "fashion," "virtual," "brand," "platform," "digital," "world," "Zepeto," "company," and "game." After analyzing TF-inverse document frequency(TF-IDF), "virtual" was the most important, followed by "brand," "platform," "Zepeto," "digital," "world," "industry," "game," "fashion show," and "industry." "Metaverse" and "fashion" were found to have a high TF but low TF-IDF. Further, words such as "virtual," "brand," "platform," "Zepeto," and "digital" had a higher TF-IDF ranking than TF, indicating that they had high importance in the text. Second, convergence of iterated correlations analysis using UNICET revealed four clusters, classified as "virtual world," "metaverse distribution platform," "fashion contents technology investment," and "metaverse fashion week." Fashion brands are hosting virtual fashion shows and stores on metaverse platforms where the virtual and real worlds coexist, and investment in developing metaverse-related technologies is under way.