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Is Text Mining on Trade Claim Studies Applicable? Focused on Chinese Cases of Arbitration and Litigation Applying the CISG

  • Yu, Cheon;Choi, DongOh;Hwang, Yun-Seop
    • Journal of Korea Trade
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    • v.24 no.8
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    • pp.171-188
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    • 2020
  • Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

The Effect of Cohesive Devices on Memory and Understanding of Scientific Text (응집장치가 과학텍스트의 기억과 이해에 미치는 효과)

  • 김세영;한광희;조숙환
    • Korean Journal of Cognitive Science
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    • v.13 no.2
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    • pp.1-13
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    • 2002
  • This Paper is concerned with the impact of linguistic markers of coherence, such as causal connectives. repetitions. and anchoring devices. on the comprehension of a scientific text in Korean. A scientific text on the process of lightning formation was selected. and two versions of the text were constructed by varying the strength of coherence. Eighty-two undergraduate students took Part in the experiment in which they were instructed to fill in the blanks in each text in a recall and a recognition task and to respond to a set of question in a comprehension test. The results of this experiment revealed a selective effect of the cohesive markers. It was found that the different linguistic signals seem to Play a facilitating role in varying degrees in accordance with the type of tasks involved Moreover an analysis of topic continuity from the beginning paragraphs through the last revealed that the text was better understood in the paragraphs containing the main topic better than those without it. This finding seems to indicate that the off-line processing of scientific text is not influenced solely by the local bottom-up processing alone The effect of topic continuity seems to suggest that a global. top-down processing effect has an important role to play. overriding the impact of cohesive devices.

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Analysis of Adverse Drug Reaction Reports using Text Mining (텍스트마이닝을 이용한 약물유해반응 보고자료 분석)

  • Kim, Hyon Hee;Rhew, Kiyon
    • Korean Journal of Clinical Pharmacy
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    • v.27 no.4
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    • pp.221-227
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    • 2017
  • Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

A Gaussian Mixture Model for Binarization of Natural Scene Text

  • Tran, Anh Khoa;Lee, Gueesang
    • Smart Media Journal
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    • v.2 no.2
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    • pp.14-19
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    • 2013
  • Recently, due to the increase of the use of scanned images, the text segmentation techniques, which play critical role to optimize the quality of the scanned images, are required to be updated and advanced. In this study, an algorithm has been developed based on the modification of Gaussian mixture model (GMM) by integrating the calculation of Gaussian detection gradient and the estimation of the number clusters. The experimental results show an efficient method for text segmentation in natural scenes such as storefronts, street signs, scanned journals and newspapers at different size, shape or color of texts in condition of lighting changes and complex background. These indicate that our model algorithm and research approach can address various issues, which are still limitations of other senior algorithms and methods.

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Analyzing User Feedback on a Fan Community Platform 'Weverse': A Text Mining Approach

  • Thi Thao Van Ho;Mi Jin Noh;Yu Na Lee;Yang Sok Kim
    • Smart Media Journal
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    • v.13 no.6
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    • pp.62-71
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    • 2024
  • This study applies topic modeling to uncover user experience and app issues expressed in users' online reviews of a fan community platform, Weverse on Google Play Store. It allows us to identify the features which need to be improved to enhance user experience or need to be maintained and leveraged to attract more users. Therefore, we collect 88,068 first-level English online reviews of Weverse on Google Play Store with Google-Play-Scraper tool. After the initial preprocessing step, a dataset of 31,861 online reviews is analyzed using Latent Dirichlet Allocation (LDA) topic modeling with Gensim library in Python. There are 5 topics explored in this study which highlight significant issues such as network connection error, delayed notification, and incorrect translation. Besides, the result revealed the app's effectiveness in fostering not only interaction between fans and artists but also fans' mutual relationships. Consequently, the business can strengthen user engagement and loyalty by addressing the identified drawbacks and leveraging the platform for user communication.

Analysis of Social Network Service Data to Estimate Tourist Interests in Green Tour Activities

  • Rah, HyungChul;Park, Sungho;Kim, Miok;Cho, Youngbeen;Yoo, Kwan-Hee
    • International Journal of Contents
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    • v.14 no.3
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    • pp.27-31
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    • 2018
  • Social network service (SNS) data related to green tourism were used to estimate preferred tour sites and users' interests. Keywords related with green tour activities were employed to search the SNS data. SNS data were collected from Korean blogs such as Naver and Daum from June $1^{st}$ to August $31^{st}$ between 2015 and 2017 using text-mining solution. During the study period, seven hundred and five posts were analyzed. Associated words that frequently co-occurred with keywords were classified into different categories depending on the nature of associated words. Associated words included swimming pools and camping sites (location); experience and swimming pools (attribute); and water play and culture (culture/leisure). Our data suggest that SNS users with experience of green tourism in Korea exhibited interest in green tourism with swimming pools, camping sites, experience, water play and/or culture rather than particular popular sites. Based on the findings, it is recommended that preferred facilities such as swimming pools should be provided at green tourism sites to meet the users' needs and to facilitate green tourism.

The Effect of the Science Process Skills and Science Related Attitude on the Science-play through the Science Class (과학 놀이를 이용한 과학수업이 과학 탐구 능력과 과학 관련 태도에 미치는 영향)

  • Heo, Kwi-Hee;Lee, Ji-Hwa;Moon, Seong-Bae
    • Journal of the Korean Society of Earth Science Education
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    • v.7 no.1
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    • pp.1-10
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    • 2014
  • The purpose of this study is to introduce the science-play in the regular class, stimulate the student's curiosity, motivate them and take active part in their science class. To make an effective science class, we developed the science-play activity instead of experiments in the text, and applied it to the class. The experimental group has statistically meaningful results in the science process skills, expecially in subordinate elements such as observation, deduction, expectation, data analysis and assumption establishments(p<.01). However, the comparative group has no meaningful results in the science process skills. Though the average value of the science related attitude in the experimental group had only a little increase and had no statistically meaningful results, that in the comparative group has decreased during the same period. As for the experimental group, the science-play activities were repeated and their science related attitude has increased a little. Even though there were no meaningful statistic results(p>.05), the science-play activity was effective in the science related attitude. As a result of this research, it could be said that the science-play activity can improve the student's science process skills and the science related attitude, and the science-play program should be further developed and applied to make easy and effective science classes.

Study on Self-Reflexivity of Changgeuk Seopyenje (창극 <서편제>의 자기반영성 연구)

  • LEE, JINJOO
    • (The) Research of the performance art and culture
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    • no.32
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    • pp.333-370
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    • 2016
  • This study examines self-reflexive scenes of Changgeuk [창극; Korean classical opera] Seopyenje [서편제]. This show deals with Pansori [판소리; a kind of Korean folk play] and its singers. The uniqueness of this show is that although it is a new creative work of Changgeuk, the traditional Pansori music is used intactly. These characteristics are related to some self-reflexive scenes in the show: the narcissistic reference of Pansori makes to seem that this show inherits a artistry of Pansori; a play within a play and a role-play reinforce a reality on the action and characters of outer play; an intertextuality, bringing the narrative and music of Pansori Simcheong-ga [심청가] in this show, it makes audiences spontaneously discover a connection between the cited original text and the hypertext. Namely, the self-reflexivity of Changgeuk Seopyenje doesn't destroy an illusion, but rather it presents a kind of conservative self-reflexivity which uncovers a part of tricks for the illusion in order to create new illusion.