• Journal of Gifted/Talented Education
• /
• 제12권2호
• /
• pp.1-15
• /
• 2002

The purpose of this study was to investigate the characteristics of preschoolers' giftedness by their parents' perception. Total 3 groups of 148 subjects from age 30 months to 6 years 10 months old young gifted children's parents participated. The major findings were as follows : (1) There were critical characteristics of preschoolers' giftedness by parents' perception, which were 'good memory', 'high curiosity', 'read and understand of math', 'enjoy of learning and high motivation', 'high concentration', reading books', 'verbal ability', 'creativity', 'questions', and 'independency', (2) These characteristics of preschoolers' giftedness showed more strong and intense as they got older, and (3) Some characteristics revealed more, but the other characteristics revealed less as they got older. These findings suggested the consideration of child's age as the reliable identification process of young gifted children.

• Journal of Intelligence and Information Systems
• /
• 제21권2호
• /
• pp.69-92
• /
• 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 the Korean Academy of Child and Adolescent Psychiatry
• /
• 제12권1호
• /
• pp.103-114
• /
• 2001

Tourette's disorder is a disease which manifests one or more motor tics and vocal tics for more than a year. Chronic motor tic or vocal tic disorders are characterized by only one kind of tics for more than a year. We intended to investigate the clinical characteristics of the patients with chronic motor tic disorders or Tourette's disorders who had admitted from May 1, 1998 to May 1, 1999 to Seoul National University Hospital Child and Adolescent Psychiatry ward. In addition, we compared the clinical characteristics of the patients in order to elucidate the relationship between the two disorders. The patients with learning disabilities were selected as controls. There was no statistically significant difference between the onsets of the patients with chronic motor tic disorders(n=13, $7.3{\pm}2.5$ years), and Tourette's disorder(n=39, $7.2{\pm}2.2$ years), but with learning disability($4.2{\pm}1.9$ years). Also, the patients with chronic motor tic disorder and Tourette's disorder showed similar age at admission($11.7{\pm}2.7$ versus $11.5{\pm}2.6$ years), duration of admission($5.7{\pm}5.4$ versus $11.0{\pm}8.7$ weeks), mothers' ages at child birth($27.3{\pm}2.9$ versus $28.3{\pm}6.7$ years old),and fathers' age at child birth($32.2{\pm}3.2$ versus $33.3{\pm}5.2$ years old). We observed that those who had learning disabilities were alike in those aspects, except for age at visit to clinic($9.8{\pm}3.2$ years old). Family history of psychiatric illnesses(24.1% versus 46.2%), recognized precipitating factors(11.1% versus 35.7%) and response to pharmacological treatments(77.8% versus 76.9%) of the patients with chronic motor tic disorders and Tourette's disorders were observed and no differences were found. Comorbid patterns of diseases were noted. Intrafamilial conflicts were more common in the patients with learning disabilities than those with chronic tic disorders or Tourette's disorders. Precipitating factors were observed more frequent in chronic tic disorder and Tourette's disorder than learning disability. Neurocognitive profiles were investigated, and verbal IQs of the patients with chronic motor tic disorder, Tourette's disorder and learning disability were $92.3{\pm}10.7$, $94.7{\pm}14.9$, $94.3{\pm}13.8$, performance IQs $93.0{\pm}20.5$, $97.5{\pm}13.0$, $95.0{\pm}16.9$ and full-scale IQs $91.9{\pm}20.1$, $95.8{\pm}14.5$, $93.9{\pm}15.1$, respectively, which were found to be not significantly different. No difference was found in structural neurological abnormalities and EEG profiles. The patients with learning disabilities showed more common Bender-Gestalt test abnormalities. In conclusion, we have not found any affirmative clues for the division of chronic motor tic disorder and Tourette's disorder in clinical perspective.