• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Horticultural Science & Technology
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• v.29 no.5
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• pp.456-460
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• 2011

This study was carried out to compare the pattern of mutant variation and to evaluate the characteristics of mutants obtained by gamma irradiation in rose 'Kardinal'. Forty four rooted cuttings of 'Kardinal' were irradiated at 70 Gy gamma-ray dose from a $^{60}Co$ source to induce mutants in 2002. The irradiated plants were planted in field, and observed spotting of petal color mutants from 2002 to 2004. Four different kinds of mutant twigs with each different color flower were obtained from the irradiated 'Kardinal' with red petal. After being identified to be a stable mutant from 2004 to 2008, each mutant line propagated by cutting was hydroponic-cultured to evaluate the characteristics in the greenhouse from 2008 to 2009. Four mutant lines obtained from 'Kardinal' with red petal (Red group, 44A, 45B) include KA1 with light pink petal (Red group, 55B-55D), KA2 with pink petal (Red group, 63A-63B), KA3 with deep pink (Red purple, N57A-N57C), and KA4 with orange red (Red group, 43A-43B). Diameters of each flower in four mutant lines were different from 'Kardinal'. The line KA1 was 9.5 cm wide, and it showed the smallest diameter when compared to other mutants. While the line KA2 was the largest one with 12.5 cm 'Kardinal'. Petal number per flower was also variable among the mutants. The line KA2 had 39.8 petals being the largest number among the mutants, while the line KA1 was the lowest one compared to 35.5 petals of 'Kardinal'. Petal color was measured by using colorimeter. Brightness (L) measured at each petal of four mutants increased more than 'Kardinal'. CIE Lab values, a and b decreased more than 'Kardinal' at the petal color of three mutants except the line KA4. Characteristics of shoot, leaf, etc. from four mutants were also different from the ones of 'Kardinal'. The line KA1 was shortest in shoot, node and peduncle length, and lowest in prickle number. The reverse side of leaves was reddish green color in 'Kardinal' as well as the line KA4, but green color in the line KA1, KA2, and KA3.

• Journal of the Korean Geographical Society
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• v.28 no.3
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• pp.247-267
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• 1993

According to developing international economy since the World War II, the increase and competition of the national business is so empha-sized tht both the interest and the necessity about marine transportation playing the impor-thant role of international transportation are increased. Today, the container transportation, as called the innovation of marine transport has been prevailed since the 1970's. The purpose of this paper is to grasp the spatial structure of the hinterlands and forelands, its object is export container cargo at Pusan Export Port, as known for the transportation node of modern containerlization. In this study, for the purpose of grasping the relation between hinterlands and forelands of Korean export container cargo, first, I researched the transition of carloading about container cargo, the bistribution channel of cargo, the change of the items of container and the carlo-adings about transport route, secondly, I used the cluster analysis so as to group hinterlands according to the items of goods and forelands. The object of the analysis is container cargo of Choyang Line, Hanjin Shipping and Hyundai Merchant Marine of National Frag Carriers. The source materials used in this study are Trucking Data of Hanjin Co., Container Ren-tal Data of Samik Transport Co. and Transpor-ting Present Condition Tables of Hyundai Mer-chant Marine. 1. There are two kinds of the transport classi-fied by its form: FCL and LCL. In Pusan Con-tainer Export, a lot of textile goods, clothings and furniture, compound, electric goods, and so on are dealed with but the rate of occupation of the transport is getting lower while that of occupation of equipment, papers and agricultu-ral, mineral and livestock industry higher. 2. In 1990, the transports of container cargo in Korea consist of 7 services and round-the world lines. We can list North America lines, East-South Asian lines, Japan lines and Inter European lines, in order of the quantity of tran-sport form the largest to the smaller. We can have another list that Japan lines, North Ame-rica lines and East-South lines in order of the rate participation of national flag carriers, be-cacuse Korean foreign trade lay disproportionate emphasis on East-South Asian lines. Japan lines among them is the biggest import-export market. Since the rationlization policy of marine tran-sport in 1984, each of national flag carriers have its own lines. Hanjin Shipping predominates over North America lines, Choyang Line over New Zealand, Inter European and Austria lines and Hyundai Merchant Marine over Center-South America lines, in terms of the volume of transport. And small-to-medium sized shippers are prevailing in lines which are adjacent to Korea, Such as Japan lines and East-South Asian lines. 3. In relation to hinterlands and forelands of Choyang Line, the light industry goods, electric goods and machinary produced in Seoul and Pusan are exported to the major ports in Europe and Japan, the same produces in Suwon, Ulsan, Kumi are exported to European Ports, and those in Incheon and Kwangju Austrian and Japanese ports, and those in the rest regions to the major port in Japan. 4. In relation to hinterlands and forelands of Hanjin Shipping, the light industry goods pro-ducing in Seoul and Pusan, the electric goods and machinary in Incheon and Pyeongteck, are exported to New York and Los Angeles. Electric goods and machinary Masan, Anyang, Cheona, Cheongju and Incheon, Electric goods machinary and light industry goods in Kwangju and non mental goods in Pohang, are exported New York, Los Angeles and Oakland. 5. In relation to hinterlands and forelands of Hyundai Merchant Marine, the region of Seoul, Pusan and Incheon closely related with the main ports in U.S.A. The rest regions with Montreal. The hinterlands of export container cargo can be classified by its export items into three kinds: the large city, industrial city and the rest city. Choyang Line's forelands are European lines, Japan lines and Austria lines, and Hanjin Shipping's forelands are North America lines, and Hyundai Merchant Marine's forelands are North America lines and Japan line. 3 National flag carriers' major forelands are determined by the size of port and the shipper's convenient use of the port terminal.

• Korean Journal of Head & Neck Oncology
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• v.14 no.2
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• pp.236-243
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• 1998

Objectives: Local invasion of the thyroid cancer that is invasion of the upper aerodigestive tract, neurovascular structures of the neck and superior mediastinum, is infrequent and comprises of 1-16% of well-differentiated thyroid cancer. However the proximity of the thyroid gland to these structures provides the means for an invasive cancer to gain ready access into theses structures and when invasion occurs, it is the source of significant morbidity and mortality. So locally invasive thyroid cancer should be removed as much as possible, but still much debates have been exist whether the surgical method should be radical or conservative. This study was desinged to evaluate the clinical characteristics and the surgical treatment of the locally invasive thyroid cancer. Material and Methods: At the department of otorhinolaryngology of Hallym university, 10 patients diagnosed as locally invasive thyroid cancer among the 81 patients treated for thyroid cancer between 1991 to 1997 were retrospectively evaluated. Results: Of the 10 patients, 3 patients had histories of previous surgical treatment with or without radiation or radioactive iodine therapy. The site of invasion of thyroid cancer were trachea(7 cases), recurrent laryngeal nerve(5 cases), mediastinal node(5 cases), esophagus(3cases), larynx(3cases), carotid artery(3 cases), pharynx(l case), and other sites(4 cases). The operation techniques included 1 partial laryngectomy and 1 partial cricoid resection, 2 shavings and 3 window resections of the trachea, 1 sleeve resection of the trachea with end-to-end anastomosis and 1 cricotracheoplasty for tracheal invasion, 2 shavings and 1 partial esophagectomies for esophageal invasion, and 1 wall shaving and 2 partial resections with $Gortex^{\circledR}$ tube reconstruction for carotid artery invasion, and so on. Conclusions: These data and review of literature suggest that the surgical method should be perfomed on the basis of individual condition and complete removal of all gross tumor with preservation of vital structures whenever possible will offer a good result.