• Cross-Cultural Studies
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• v.27
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• pp.197-224
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• 2012

The purpose of this study is to analyze the issues concerning feminine forms of nouns which indicates occupations in French. In distinguishing the French masculine and feminine forms, many linguistic issues about feminizing occupational nouns became a hot issue among scholars. However, reasonably logical and effective methods to solve such issues are not suggested yet. The first part is focused on how the feminine forms were historically altered to investigate changing process of nouns representing jobs. Through this, we found that the occupational nouns' feminization is quite related to the social big changes, the woman's social condition and reality reflecting on languages periodically. We discussed the important factors deciding such changes, such as semantic, linguistic and sociolinguistic causes, in the second part of the study. And we mentioned issues which can be suggested in investigating grammatical rules of the feminine form of occupational nouns. The last part is on plans to learn the feminine form of occupations in an effective way. The language is being developed while it is closely related with social and cultural environment of people who use the languages. In this meaning, occupational nouns' feminization is a good example which can reflect chronological and social changes. Through the thesis, we know that it is not enough to provide explanation of changes of feminine occupational nouns about the woman's social roles' alteration. We just hope it can be at least a small help in doing more systematic and deeper analysis which can show the fact that languages reflect the phenomenon of social changes.

• 건강소식
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• v.34 no.11
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• pp.6-9
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• 2010

가을이 익어가는 무렵, 삼청동 길은 아름다웠다. 갑작스레 장소가 변경된 탓에 혼란이 있기는 했지만 그 모든 일상의 번거로움을 상쇄할 만큼 무르익는 가을의 삼청동은 충분히 아름다웠다. 배우 지진희와의 만남은 그 감상의 연장선 위에 있었다. 그날의 쾌청한 공기와 설익은 낙엽 내음, 적당한 햇살과 멋들어진 카페, 그리고 몸과 마음이 건강한 배우라는 직업의 한 남자. 모든 것이 조화로웠고 부드러웠다.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.33-46
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• 2021

Using news big data analysis, this study was aimed to examine the suicide phenomena in Korean society, and to evaluate whether suicide prevention policies reflect social phenomena appropriately. For this purpose, 9,142 news titles with suicide as the keyword were collected from eight central newspapers between 2000 to 2018. Nouns were extracted, and data was refined for network analysis. The total period was divided into 4 periods based on the 1st and 2nd suicide prevention policies, and the characteristics of suicide phenomena in each period were identified through the top 50 frequent main words and the clusters. As a result, period 1 (2000~2003) with 6 clusters (military, internet environment, economic problems, pessimism, school, corruption), period 2 (2004~2008) with 8 clusters (high social class, school, economic problems, suicide attempts, family issues, social problems, military, responsibilities), period 3 (2009~2013) with 6 clusters (school, family issues, suicide attempts, occupation, military, investigation), and period 4 (2014~2018) with 8 clusters (military, suicide insurance money, family issues, suicide attempts, occupation, job stress, celebrity, corruption) were identified. Study results suggested the characteristics of suicide phenomena in our society. Further, the appropriateness of the implementation of suicide prevention policies was discussed.

• 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.