• Journal of Music and Human Behavior
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• v.4 no.2
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• pp.84-105
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• 2007

Cerebral palsy is a collection of motor disorders resulting from damage to the central nervous system that arise in multiple handicaps including cognitive disorders, speech disorders, epilepsy, perception disorders, and emotion disorders. Today spastic cerebral palsy has become more prevalent because intensive care for newborns has resulted in higher survival rates for very small premature babies. Since the children grow the fastest in order for a development during one year after birth, the therapeutic intervention is provided as early as possible to the children with cerebral palsy. After seven year old, there is no effect of intervention. So, the necessity of early intervention to spastic cerebral palsied infants is increasing. The purpose of this study is to develop the music therapy activity program using the techniques of neurological music therapy(NMT), the therapeutic application of music to dysfunctions due to neurologic disease of the human nervous system, for rudimentary movement phase of spastic cerebral palsied infant. This music therapy activity program was developed on the basis of the major developmental tasks of the rudimentary movement phase, the period that children can acquire the most basic movement function at the 0 to 2. Then the developmental characteristics of spastic cerebral palsy were applied to this music therapy activity program. This music therapy activity program was classified to three domains, those are stability, locomotion, and manipulation. This study has been consisted of three steps, those are the development of the activities, the evaluation of the activities by th panels, and the adjustment and complement of the activities. Reviewing literatures and interviews were done for the development of the activities, and the evaluation the activities was done by seven music therapists. In the evaluation steps, the questionnaire was used for estimating the content validity and application efficiency. The adjustment and complement of the activities were evaluated by the panels who were participating in the music therapy for cerebral palsied children in the clinical setting, and the results of the adjustment and complement were confirmed by the panels. The evaluation was presented in a mean value with the comment of the panels. In conclusion, the music therapy activity program for the spastic cerebral palsied infants using the techniques of NMT was developed on the basis of the major developmental tasks of the rudimentary movement phase. The program is comprised of 38 activities, those are 14 activities for developing the stability, 10 activities for developing the locomotion, and 14 activities for developing the manipulation. The programed activities would bring out the answers in the affirmative for the conformance with infants' development phase, the harmony between the objective and the activity, the conformance with the cerebral palsied infants, the properness of the music and the instruments, and the utility in the clinic field. This results mean that this developed music activity program is appropriate to help spastic cerebral palsied infants progress their movement development by stages.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.2
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• pp.15-28
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• 2021

'Cheonunjeongsa (天雲精舍)', designated as Gyeongsangbukdo Folklore Cultural Property No. 76, is a Siksanjeongsa built in 1700 by Manbu Lee Shiksan. In this study, we investigate the life and perspective of Manbu Lee in relation to Siksanjeongsa, and estimate the feng shui location, territoriality, and original landscape by analyzing 「Nuhangnok」 and 「Nuhando」, the results of his political management. The following results were derived by examining the philosophy that the scholar wanted to include in his space. First, Manbu Lee Shiksan was a representative hermit-type confucian scholar in the late Joseon Dynasty. 'Siksan', the name of the government official and the nickname of Manbu Lee, is derived from the mountain behind the village, and he wanted to rest in the four areas of thought(思), body(躬), speech(言), and friendship(交). During the difficult years of King Sukjong, Lee Manbu of a Namin family expressed his will to seclude through the title 'Siksan'. Second, There is a high possibility of restoration close to the original. Manbu Lee recorded the location of Siksanjeongsa, spatial structure, buildings and landscape facilities, trees, surrounding landscape, and usage behaviors in 「Nuhangnok」, and left a book of 《Nuhangdo》. Third, Manbu Lee refers to the feng shui geography view that Oenogok is closed in two when viewed from the outside, but is cozy and deep and can be seen from a far when entering inside. The whole village of Nogok was called Siksanjeongsa, which means through the name. It can be seen that the area was formed and expanded. Fourth, the spatial composition of Siksanjeongsa can be divided into a banquet space, an education space, a support space, a rest space, a vegetable and an herbal garden. The banquet space composed of Dang, Lu, and Yeonji is a personal space where Manbu Lee, who thinks about the unity of the heavenly people, the virtue of the gentleman, and humanity, is a place for lectures and a place to live. Fifth, Yangjeongjae area is an educational space, and Yangjeongjae is a name taken from the main character Monggwa, and it is a name that prayed for young students to grow brightly and academically. Sixth, the support space composed of Ganjijeong, Gobandae, and Sehandan is a place where the forested areas in the innermost part of Siksanjeongsa are cleared and a small pavilion is built using natural standing stones and pine trees as a folding screen. The virtue and grace of stopping. It contains the meaning of leisure and the wisdom of a gentleman. Seventh, outside the wall of Siksanjeongsa, across the eastern stream, an altar was built in a place with many old trees, called Yeonggwisa, and a place of rest was made by piling up an oddly shaped stone and planting flowers. Eighth, Manbu Lee, who knew the effects of vegetables and medicinal herbs in detail like the scholars of the Joseon Dynasty, cultivated a vegetable garden and an herbal garden in Jeongsa. Ninth, it can be seen that Lee Manbu realized the Neo-Confucian utopia in his political life by giving meaning to each space of Siksanjeongsa by naming buildings and landscaping facilities and planting them according to ancient events.

• 한국노년학
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• v.30 no.3
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• pp.973-992
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• 2010

The hearing impairment is the representative disorder that affects the quality of the routine life of the aged period. This study was aimed to develop the Korean evaluation scale for hearing handicap(KESHH) with which we can evaluate social and psychological effects of the hearing impairment. Applying this scale clinically, we can analyze the geriatric hearing loss specifically and improve the quality of the aural rehabilitation that can help the hardness of the hearing impairment. Data were collected from 288 participants(176 hearing aid users and 112 non-hearing aid users) and the average age of the participants was 67.4 years old ( 60.15 for the hearing aids users and 78.9 for the non hearing users). The composition ratio of the male and female participants were 58.0% and 42.0% and extrovert and introvert personality were 49.3% and 50.7% showing balanced formation. The tentative draft of KESHH measurements were produced with 30 items and following 5 subscales. Using factor analysis, 6 items were erased and 4 subscales - social effect, psycho/emotional effect, interpersonal effect, and perception of hearing aids - were identified. As each subscale consisted of 6 items, 24 items were corrected and remained totally. Conclusively, the KESHH was developed with 24 items and 4 subscales including 6 items on each subscale. In addition, the KESHH was divided into type-1 and 2 depending on hearing aid users and non hearing aid users. The results of this study can be summarized as the following 5 parts. Firstly, the reliabilities of the KESHH were proved to be high because the subscales' Cronbach alpha values were from 0.723 through 0.895. Secondly, the KESHH showed systematically increasing score as the hearing impairment increased. The lowest score was 24 and the highest score was 117 and the average scores of the hearing impaired and non-hearing impaired are 72.06(SD=15.67) and 66.98(SD=20.94) showing 5.08 increased score for the hearing impaired. Depending on the degree of the hearing loss, the scores recorded 52.63 at the below of the mild hearing loss, 67.29 for the moderate hearing loss, 71.89 for the moderately severe hearing loss, and 75.57 for the severe hearing loss The comparison of the scores by hearing levels indicated that the higher the hearing levels were, the higher the scores of the KESHH with statistical significance(p<0.001). Thirdly, the correlation among 4 subscales was 0.384~0.880(p<0.001). Also, the pure tone average, personality, and the four subscales correlations showed statistical significance with 0.148~0.880 except for the pure tone average and personality and the pure tone average and perception of hearing aids. Fourthly, the total variances explained for the independent subscles were analyzed with multiple regression. The social effect was explained 17.4% with pure tone average, personality, and the status of hearing aid use variances. The psycho/emotional effect was explained 14.4% with puretone average, personality, and age variances. The interpersonal effect was explained 11.2% with pure tone average, personality, and the status of hearing aid use variances. The perception of hearing aids effect was explained 2.2% with only personality. Finally, test-retest reliability was proved to be high with 0.791(p<0.001). Conclusively, the KESHH that was developed considering Korean culture can be a useful instrument for expressing the hearing handicaps of the Korean aged hearing impaired in scores for both hearing aid users and non-users. Also, it is thought that the KESHH is useful clinically for identifying the changes of the hearing handicap scores before and after wearing hearing aids and aural rehabilitation at diverse situations.