• Journal of Navigation and Port Research
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• v.41 no.3
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• pp.109-118
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• 2017

The key word (KW) is a set of words to clearly express the important causations of marine accidents; they are determined by a judge in a Korean maritime safety tribunal. The selection of KW currently has two main issues: one is maintaining consistency due to the different subjective opinion of each judge, and the second is the large number of KW currently in use. To overcome the issues, the systematic framework used to construct KW's needs to be optimized with a minimal number of KW's being derived from a set of Common Words (CW). The purpose of this study is to identify a set of CW to develop the systematic KW construction frame. To fulfill the purpose, the word reduction method to find minimum number of CW is proposed using P areto distribution function and Pareto index. A total of 2,642 KW were compiled and 56 baseline CW were identified in the data sets. These CW, along with their frequency of use across all KW, are reported. Through the word reduction experiments, an average reduction rate of 58.5% was obtained. The estimated CW according to the reduction rates was verified using the Pareto chart. Through this analysis, the development of a systematic KW construction frame is expected to be possible.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

• Smart Media Journal
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• v.9 no.1
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• pp.51-59
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• 2020

From PC communication to the development of the internet, a new term has been coined on the social media, and the social media culture has been formed due to the spread of smart phones, and the newly coined word is becoming a culture. With the advent of social networking sites and smart phones serving as a bridge, the number of data has increased in real time. The use of new words can have many advantages, including the use of short sentences to solve the problems of various letter-limited messengers and reduce data. However, new words do not have a dictionary meaning and there are limitations and degradation of algorithms such as data mining. Therefore, in this paper, the opinion of the document is confirmed by collecting data through web crawling and extracting new words contained within the text data and establishing an emotional classification. The progress of the experiment is divided into three categories. First, a word collected by collecting a new word on the social media is subjected to learned of affirmative and negative. Next, to derive and verify emotional values using standard documents, TF-IDF is used to score noun sensibilities to enter the emotional values of the data. As with the new words, the classified emotional values are applied to verify that the emotions are classified in standard language documents. Finally, a combination of the newly coined words and standard emotional values is used to perform a comparative analysis of the technology of the instrument.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.4
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• pp.149-154
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• 2014

A user should pick up relevant answers by himself from various search results when using user participation question answering community like Knowledge-iN. If refined answers are automatically provided, usability of question answering community must be improved. This paper divides questions in Q&A documents into 4 types(word, list, graph and text), then proposes summarizing methods for each question type using document statistics. Summarized answers for word, list and text type are obtained by question clustering and calculating scores for words using frequency, proximity and confidence of answers. Answers for graph type is shown by extracting user opinion from answers.

• Fashion & Textile Research Journal
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• v.14 no.1
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• pp.75-82
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• 2012

Compared to off-line, on-line word-of-mouth has fast dissemination speed and extensive effects based on interactive features. Moreover, in the age of Web 2.0, on-line media has made consumers evolve from simple purchasers to producers, who intervene into product manufacturing through on-line WOM. According to this, this study is conducted to clarify how consumer's prosumer propensity affects WOM in detail when purchasing fashion products on-line through website interactivity and perceived usefulness of WOM. The results are as follows. 1. Consumer's prosumer propensity is classified in four dimensions: the propensity to participate, the propensity to relate, the propensity to amuse and the propensity to create. 2. The sample has shown low prosumer propensity overall, and there were no gender differences. 3. Testing structural equation model, it was clarified that the higher the consumer's prosumer propensity, the higher the consumer's evaluation of website interactivity and thus the greater the WOM effect through its perception of usefulness. 4. There were some differences in the path of structural equation model according to consumer's prosumer propensity. From the results, it can be concluded that consumer's prosumer propensity is a key factor in the on-line WOM. Therefore fashion businesses should actively utilize consumer's prosumer propensity to apply their opinion in the product planning stage or use it as the means of company-friendly viral marketing.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.3
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• pp.42-50
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• 2013

Brand Equity plays a major role in the highly competitive Quick Service Restaurants (QSR) industry in India. There are a variety of factors which affect the brand equity that a company commands in the market. Integrated Marketing Communication (IMC) is an emerging concept in marketing wherein all the major promotional activities are used to create a synergic output and send across a clear and consistent message to the customers. This study aims to find out the impact of five major tools of IMC namely Advertising, Word of Mouth, Sales Promotion, Event Sponsorships and Public Relations which are most applicable in the QSR industry on the four major dimensions namely Brand Awareness, Brand Association, Perceived Quality and Brand Loyalty which aid in creating Brand Equity. The study was conducted by collecting data from a sample and analyzing the data using statistical tools to find any relationships between the above mentioned variables. The findings suggest that marketers should focus on building favorable opinion about the brand amongst customers and take care regarding the news published about the brand since it affects brand image. Moreover, it was also found out that making people aware about the brand and the perceived quality about the brand play the major role in creating brand equity more than other factors.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.99-114
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• 2019

In this study we examined the preferences of eight college living factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the analysis results of text mining were visualized as word cloud. The college life factors included eight topics that were closely related to students: 'my present', 'my 10 years later', 'friendship', 'college festival', 'student restaurant', 'college dormitory', 'KNCAF', and 'long-term field practice'. In the text submitted by the students, we have established a dictionary of positive words and negative words to evaluate the preference by classifying the emotions of positive and negative. As a result, KNCAF students showed more than 85% positive emotions about the theme of 'student restaurant' and 'friendship'. But students' positive feelings about 'long-term field practice' and 'college dormitory' showed the lowest satisfaction rate of not exceeding 60%. The rest of the topics showed satisfaction of 69.3~74.2%. The gender differences showed that the positive emotions of male students were high in the topics of 'my present', 'my 10 years later', 'friendship', 'college dormitory' and 'long-term field practice'. And those of female were high in 'college festival', 'student restaurant' and 'KNCAF'. In addition, using text mining technique, the main words of positive and negative words were extracted, and word cloud was created to visualize the results.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.6
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• pp.27-38
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• 2015

This study is for deriving an evaluation system which fits to the domestic urban river. For this, two times of Delphi survey was conducted to various experts who are eminent for ecology, urban design, governance, landscape architecture, hydrology. The purpose was for analysing validity and getting extra opinion of evaluation items which were preferentially have chosen. Reflecting $1^{st}$ survey's opinions as changing the word or explaining more details, the second survey was conducted, In this time, all evaluation items were analysed as valid and experts agreed with that. In conclusion, the evaluation items, "Amenity", "Biodiversity", "Ecosystem service", "Governance", "Management", which are for evaluating domestic urban river environment were derived.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.