• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.1
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• pp.81-94
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• 2003

Objectives：Developmental reading disorder is a condition which manifests significant developmenttal delay in reading ability or persistent errors. About 3-7% of school-age children have this condition. The purpose of the present study was to validate the diagnostic values of Word/Nonword Reading Test and Letter-Symbol Discrimination Task for the purpose of overcoming the caveats of Basic Learning Skills Test. Methods：Sixty-three reading-disordered patients(mean age 10.48 years old) and sex, age-matched 77 normal children(mean age 10.33 years old) were selected by clinical evaluation and DSM-IV criteria. Reading I and II of Basic Learning Skills Test, Word/Nonword Reading Test, and Letter-Symbol Discrimination Task were carried out to them. Word/Nonword Reading Test：One hundred usual highfrequency words and one hundred meaningless nonwords were presented to the subjects within 1.2 and 2.4 seconds, respectively. Through these results, automatized phonological processing ability and conscious letter-sound matching ability were estimated. Letter-Symbol Discrimination Task：mirror image letters which reading-disordered patients are apt to confuse were used. Reliability, concurrent validity, construct validity, and discriminant validity tests were conducted. Results：Word/Nonword Reading Test：the reliability(alpha) was 0.96, and concurrent validity with Basic Learning Skills test was 0.94. The patients with developmental reading disorders differed significantly from normal children in Word/Nonword Reading Test performances. Through discriminant analysis, 83.0% of original cases were correctly classified by this test. Letter-Symbol Discrimination Task：the reliability(alpha) was 0.86, and concurrent validity with Basic Learning Skills test was 0.86. There were significant differences in scores between the patients and normal children. Factor analysis revealed that this test were composed of saccadic mirror image processing, global accuracy, mirror image processing deficit, static image processing, global vigilance deficit, and inattention-impulsivity factors. By discriminant analysis, 87.3% of the patients and normal children were correctly classified. Conclusion：The patients with developmental reading disorders had deficits in automatized visuallexical route, morpheme-phoneme conversion mechanism, and visual information processing. These deficits were reliably and validly evaluated by Word/Nonword Reading Test and Letter-Symbol Discrimination Task.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.3
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• pp.61-77
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• 2011

This study focused on relevant rock names related to Sinsun(神仙) which had been settled as Taoist traces were combined with places. While interpreting major features of Sundoism relevant rocks, it also discussed types and places of rocks reflected in their names by considering distinct characteristics of landscape characters that ancestors viewed through the rocks or on the rocks. Conclusion of this study is summarized as follows. 1. Among the rock names related to Sinsun, the most frequently discovered one was Sinsunbawi(52) and followed by Sinsunbong(神仙峰: 38), Sinsundae(神仙臺: 31). Other than these, there were Gangsundae(降仙臺: 12), Sunyoodae (仙遊臺: 10) and Sasundae(四仙臺: 5). 2. In the name of Sinsundae, 'Dae(臺)' ascertains that it was located in greatly superb place in the aspects of viewpoint and appreciation where landscape superiority and overlook scenery were fair and outstanding. 3. Sinsunbong was named for a peak of mountain. At the same time, it implied a notion of worship with images of 'merging with sky' or 'looking up.' Most of time, Sinsunbong indicated the tallest rock in the mountain chain. 4. A significant number of Sinsunbong had names where legends of Sinsun's Go game or descent were originated from. It shows that 'Sinsun(仙) and Go game' used to be very important motives for folk etymology of Sinsun related rocks. Along with the Sinsundae, a number of Sinsunbawi were also turned out to exist in land and ocean with excellent marine view. 5. According to analysis of their altitudes and heights of the peaks where the rocks belong to, Sinsunbong, Sinsundae and Sinsunbawi were in order. It might indicate that the rocks were located on top of mountain or that Sinsunbong represented the mountain itself. Compared to this, Sinsundae was located in where distant panoramic views were overlooked. It was not necessarily to be in peak but in where with a great view like Taoist world. On the other hand, Sinsunbawi was located in where has fine scenery and great valley not so far from villages, which proved its name had been influenced by place feature not altitude. 6. Feature of rock with Sinsun related name is to comprise visual stability of worship object with close linkage to attitude of worshiper. Considering its deep connection with communicative method of worship object and worshiper, seemingly it was main factor to lead folk etymology of rocks with Sinsun related names. 7. Rock is an object with the greatest implication of Sinsun imagination and Sinsun rocks show most clearly the fact that Taoism, which used to be considered as inaccessible, had been actualized in a visual and realistic manner with the change of time.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.3
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• pp.40-51
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• 2011

The purpose of the study was to understand the symbol and locational meanings in building and relocating Gwangje-jeong(光霽亭) through the analysis and interpretation on the construction background, history, the location and its characteristics. Concerning physical environment, human activities, the symbol and meanings of the formal Gwangje-jeong site and the present location, the study was concluded about the site and its meaning of tradition as following. Gwangje, the name of the pavilion, represents the fidelity of Maedang(梅堂) Yangdon(楊墩) who refused as Seonbee(a man of virtue) to be tainted with the corrupt world, which was related with the situation at that time. It implies Maedang's feeling of realizing Noojeongjeyong(樓亭題詠) of Gwangje-jeong along with the high spirit of Gwangpoongjewol(光風霽月). According to the record about rebuilding Gwangje-jeong, Maedang was the very person who planted plum flowers at the pavilion and put up the tablet of its name, Gwangje. Even after his death, Gwangje-jeong was the symbol indicating Yangdon, given the triple high ground and the planting of plum flowers. Also, Sookho(宿虎) town at the entrance of Gwangje-jeong and Bokhoam(伏虎巖: a rock) at the right side of the pavilion signifies the location for praising Maedang Yangdon, and the Yangjipha's Oensi(五言詩: five words verse) engraved on the rock gives a good description about the place, Agyesa that worshiped Yangdon. As Agye-Sa(阿溪祠) where Yangdon was worshiped and praised had been abolished in the 5th year under the Kojong's reign(1868), the spirit praising Maedang had finally been used for the relocation of Gwangje-jeong. Despite the relocation of Gwangje-jeong, the old Gwangje-jeong site has remained at least for 359years at Hucheonli, and its surroundings have maintained the name 'Gwangje' as the front place name morpheme, for example, 'Gwangje-jeong,' 'Gwangje Town,' 'Gwangje Bridge' and 'Gwangje Creek,' for symbolizing the praising of Maedang. Gwangje-jeong, as the center place of solidarity among Namwon Yang's family clan, has been able to maintain its symbol and meanings in spite of relocation, mainly because of the fellowship among the descendants, family clan and alumni who respected virtuous achievements of ancestors and shared the agony of the time. In addition, the symbolism has been preserved since the spirit of Gwangpoonjewol of Yangdon and his high character were cherished along with the spirit of Bongseon(奉先) that inherited and kept virtuous achievements of ancestors.

• 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.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.