• Journal of the Korean Geographical Society
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• v.29 no.2
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• pp.166-182
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• 1994

Kyonggi Province in the late Chosun dynasty was a center of superior government offices including 'Han' River water-road transportation and was located in the middle of an 'X'-shaped arterial road network. Because of these reasons, Kyonggi Province had a faster inflow of commodities, informations and technics compared with the other province. At this period of time, every local 'Eup' (name of administrative district) had not been affected by their above administrative districts and had their own autonomy. For this reason, every 'Eup' could be developed as a town, even if its size was small when it had sufficient internal growing conditions. Moreover, the markets ('Si-Jon') in big towns and periodical markets which were spread over the Kyonggi Province played role of commercial functions of town. And because military bases for the defence of the royal capital in Kyonggi Province also took parts of a non-agricultural city role, Xyonggi Provinc had much more possibilities of growing as a town rather than the other provinces. The towns of the late Chosun Dynasty were, except the capital and superior administrative districts which were governed by the 'You-Su', small towns which had only about 3, 000-5, 000 people. Most of the town dewellers were local officials, nobles, merchants, craftmen and slaves. And the farmers who lived near town became a pseudo-towner through suburb agriculture. Among these people, the merchants were leaders of townization. The downtowns were affected by the landform and traffic roads. The most fundamental function of towns were administrative. The opcial's grade, which was dispatched to the local administrative district ('Kun' or 'Hyun'), was decided by the size of population and agricultural land of each county. Large county which was governed by a high ranking opcial had more possibilities to develop as a large town. Because they supervised other opcials of lower rank and obtained more land and population for the town. The phonomena of farm abandonment after the Japanese Invasion of Korea in 1592-1598 stimulated the development of towns for commercial function. The commercial functions of towns were evident in the Si-Jon or Nan-Jon (names of markets) in the big cities such as Hansung and Kaesung, meanffwhile in the local areas it was emerged in the shape of periodical market networks as allied with near markets (which were called as Jang-Si) or permanent markets which were grown up from periodical markets. These facts of commercial development induced the birth of commercial town. Kyonggi Province showed the weak points of its defense system during both wars (Japanese Invasion in 1592 and Manchu's Invasion in 1636). The government reinforced its defense system by adding 4 'You-Su-Bus' and several military bases. Each local districts ('Eup'), where Geo-Jins were established, were stimulated to be a town while Jin-Kwan system were, adjusted and enforced. Among Dok-Jins(name of solitary military bases), Youngjongjin was grown up as a large garrison town which only played a role of defense. The number of towns that took roles of non-agricultural functions in Kyonggi Province was 52. Among these towns, 29 were developed as big towns which had above 3, 000 people and most of these towns were located on the northwest-southeast axes of 'X'-shaped arterial trafic network in the Chosn Dynasty, This fact points out that the traffic road is one of the important causes of the development of towns. When we make hierarchy of the towns of Kyonggi Province according to its population and how many functions it had, we can make it as 6 grades. The virst grade town 'Hansung' was the biggest central town of administration, commerce and defdnse. The 2nd grade town includes 'Kaesung' which had historical inertia that it had been the capital of the Koryo Dynesty. The 3rd grade towns include some 'You- Su-Bus' such as Soowon, Kanghwa, Kwangju and also include Mapo, Yongsan and from this we can imagine that the commercial development in the late Chosun Dynasty extremely affected the townization. The 4th-6th grade towns had smiliar population but it can be discriminated by how many town functions it had. So the 4th grade towns were the core of administration, commerce and defense function. 5th grade towns had administrative functions and one of commercial and defense functions. 6th grade towns had only one of these functions. When we research and town conditions of each grades as the ratio of non-agricultural population, we can find out that the towns from the 1st grade to 4th grade show difference by degree of townization but from the 4th grade to 6th grade towns do not show big difference in general.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• The Korean Journal of Quaternary Research
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• v.20 no.1 s.26
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• pp.51-66
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• 2006

The climate of the last glacial maximum (LGM) in northeast Asia is simulated with an atmospheric general circulation model of NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. Modern climate is simulated by a prescribed sea surface temperature and sea ice provided from NCAR, and contemporary atmospheric CO2, topography, and orbital parameters, while LGM simulation was forced with the reconstructed CLIMAP sea surface temperatures, sea ice distribution, ice sheet topography, reduced $CO_2$, and orbital parameters. Under LGM conditions, surface temperature is markedly reduced in winter by more than $18^{\circ}C$ in the Korean west sea and continental margin of the Korean east sea, where the ocean exposed to land in the LGM, whereas in these areas surface temperature is warmer than present in summer by up to $2^{\circ}C$. This is due to the difference in heat capacity between ocean and land. Overall, in the LGM surface is cooled by $4{\sim}6^{\circ}C$ in northeast Asia land and by $7.1^{\circ}C$ in the entire area. An analysis of surface heat fluxes show that the surface cooling is due to the increase in outgoing longwave radiation associated with the reduced $CO_2$ concentration. The reduction in surface temperature leads to a weakening of the hydrological cycle. In winter, precipitation decreases largely in the southeastern part of Asia by about $1{\sim}4\;mm/day$, while in summer a larger reduction is found over China. Overall, annual-mean precipitation decreases by about 50% in the LGM. In northeast Asia, evaporation is also overall reduced in the LGM, but the reduction of precipitation is larger, eventually leading to a drier climate. The drier LGM climate simulated in this study is consistent with proxy evidence compiled in other areas. Overall, the high-resolution model captures the climate features reasonably well under global domain.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.14 no.4
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• pp.1-10
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• 2011

This study was conducted to compare the growth of Zoysia japonica depending on different soil treatments in Saemangeum sea dike, which is filled with dredged soil. Zoysia japonica was planted using sod-pitching method on the control plot. On plots which were treated with forest soil and soil improvement, Zoysia japonica seeds were sprayed mechanically. Sixteen months after planting, coverage rate, leaf length, leaf width, and root length were measured and analyzed. Also, three Zoysia japonica samples per plot were collected to analyze nutrient contents. Coverage rate was 100% in B treatment plot(dredged soil+$40kg/m^3$ soil improvement+forest soil), in C treatment plots (dredged soil+$60kg/m^3$ soil improvement+forest soil), and D treatment plots (dredged soil+$60kg/m^3$ soil improvement), while only 43% of the soil surface was covered with Zoysia japonica on control plots. The width of the leaf on C treatment plots (3.79mm) was the highest followed by D treatment (3.49mm), B treatment (2.40mm) and control plots (1.97mm). Leaf and root length of D treatment was 30.18cm and 13.18cm, which were highest among different treatments. The leaf length of D treatment was highest followed by C, B, and A treatments. The root length of D treatment was highest followed by C, A, and B treatments. The nitrogen and phosphate contents of the above ground part of Zoysia japonica were highest in C treatment, followed by D, B, and A treatments. The nitrogen and phosphate contents of the underground part of Zoysia japonica were highest in D treatment, followed by C, A, and B treatments. C and D treatments showed the best results in every aspect of grass growth. The results of this study could be used to identify the cost effective way to improve soil quality for soil surface fixation on reclaimed areas using grass species.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.6
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• pp.317-322
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• 2016

While PFOS sodium salt ($C_8F_{17}SO_3Na$) was not degraded by microorganisms for 28 days, the 4 alternatives were biodegraded at the rates of 21.6% for $C_{25}F_{17}H_{32}S_3O_{13}Na_3$, 20.5% for $C_{15}F_9H_{21}S_2O_8Na_2$, 15.8% for $C_{23}F_{18}H_{28}S_2O_8Na_2$ and 6.4% for $C_{17}F_9H_{25}S_2O_8Na_2$, respectively. The acute toxicity test using Daphnia magna was conducted for 48 hours, the half effective concentration ($EC_{50}$) of PFOS sodium salt ($C_8F_{17}SO_3Na$) was evaluated in 54.5 mg/L. While the 4 alternatives did not show any effect at 500.0 mg/L. The surface tension of the PFOS salt ($C_8F_{17}SO_3Na$) is 46.2 mN/m at a concentration of 500.0 mg/L. While the surface tension of the 4 alternatives was found to be superior to PFOS sodium salt ($C_8F_{17}SO_3Na$). The surface tension of $C_{23}F_{18}H_{28}S_2O_8Na_2$ (20.9 mN/m) has the lowest, followed by $C_{15}F_9H_{21}S_2O_8Na_2$ (23.4 mN/m), $C_{17}F_9H_{25}S_2O_8Na_2$ (27.3 mN/m), $C_{25}F_{17}H_{32}S_3O_{13}Na_3$ (28.2 mN/m). The four kinds of alternatives ($C_{15}F_9H_{21}S_2O_8Na_2$, $C_{17}F_9H_{25}S_2O_8Na_2$, $C_{23}F_{18}H_{28}S_2O_8Na_2$, $C_{25}F_{17}H_{32}S_3O_{13}Na_3$) were found to be superior to PFOS sodium salt ($C_8F_{17}SO_3Na$) in terms of biodegradation, Daphnia sp. acute toxicity and surface tension, and thus they were considered applicable as PFOS alternatives. Especially biodegradation rate of $C_{15}F_9H_{21}S_2O_8Na_2$, $C_{23}F_{18}H_{28}S_2O_8Na_2$ and $C_{25}F_{17}H_{32}S_3O_{13}Na_3$ was relatively high as 15.8~21.6%, and Daphnia sp. acute toxicity and surface tension were considerably superior (surface tension 39~55%) to PFOS sodium salt. Therefore, these alternatives are considered to be available as an alternative of PFOS.

• Reproductive and Developmental Biology
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• v.28 no.1
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• pp.21-27
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• 2004

This study was conducted to investigate the developmental ability of caprine embryos after somatic cell interspecies nuclear transfer. Recipient bovine and porcine oocytes were obtained from slaughterhouse and were matured in vitro according to established protocols. Donor cells were obtained from an ear-skin biopsy of a caprine, digested with 0.25% trypsin-EDTA in PBS and primary fibroblast cultures were established in TCM-199 with 10% FBS. The matured oocytes were dipped in D-PBS plus 10% FBS ＋ 7.5 $\mu$ g/ml cytochalasin B and 0.05M sucrose. Enucleation were accomplished by aspirating the first polar body and partial cytoplasm which containing metaphase II chromosomes using a micropipette with an out diameter of 20∼30 $\mu$m. A Single donor cell was individually transferred into the perivitelline space of each enucleated oocyte. The reconstructed oocytes were electric fusion with 0.3M mannitol fusion medium. After the electrofusion, embryos were activated by electric stimulation. Interspecies nuclear transfer embryos with bovine cytoplasts were cultured in TCM-199 medium supplemented with 10% FBS including bovine oviduct epithelial cells for 7∼9 day. And porcine cytoplasts were cultured in NCSU-23 medium supplemented with 10% FBS for 6 ∼8 day at $39^{\circ}C, 5% CO_2 $in air. Interspecies nuclear transfer by recipient bovine oocytes were fused with electric length 1.95 kv/cm and 2.10 kv/cm. There was no significant difference between two electric length in fusion rate(47.7 and 44.6%) and in cleavage rate(41.9 and 54.5%). Using electric length 1.95 kv/cm and 2.10 kv/cm in caprine-porcine NT oocytes, there was also no significant difference between two treatments in fusion rate(51.3 and 46.1%) and in cleavage rate(75.0 and 84.9%). The caprine-bovine NT oocytes fusion rate was lower(P＜0.05) in 1 pulse for 60 $\mu$sec(19.3%), than those from 1 pulse for 30 $\mu$sec(50.8%) and 2 pulse for 30 $\mu$sec(31.0%). The cleavage rate was higher(P＜0.05) in 1 pulse for 30 $\mu$sec(53.3%) and 2 pulse for 30 $\mu$sec(50.0%), than in 1 pulse for 60 $\mu$sec(18.2%). The caprine-porcine NT oocytes fusion rate was 48.1% in 1 pulse for 30 $\mu$sec, 45.2% in 2 pulse for 30 $\mu$sec and 48.6% in 1 pulse for 60 $\mu$sec. The cleavage rate was higher(P＜0.05) in 1 pulse for 30 $\mu$sec(78.4%) and 1 pulse for 60 $\mu$sec(79.4%), than in 2 pulse for 30 $\mu$sec(53.6%). In caprine-bovine NT embryos, the developmental rate of morula and blastocyst stage embryos were 22.6% in interspecies nuclear transfer and 30.6% in parthenotes, which was no significant differed. The developmental rate of morula and blastocyst stage embryos with caprine-porcine NT embryos were lower(P＜0.05) in interspecies nuclear transfer(5.1%) than parthenotes(37.4%).

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.