• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.509-519
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• 2017

This study addresses the effective utilization of film music as learning material for language, target culture, and ethics education. Music is intertwined with language and culture, and even with ethics. This study focuses on the potential power of film music in the processes of teaching and learning in a classroom. For this purpose, five songs are selected from the soundtrack of Disney's famous animation The Lion King: "Circle of life", "I just can't wait to be king", "Be prepared", "Hakuna Matata", and "Can you feel the love tonight?", and concrete learning activities are suggested based on these. Using these five songs, gap-filling and singing-recoding tasks are proposed as listening and speaking activities respectively. Film music is also very useful in learning vocabulary, sentence structure, and grammar. Learners participate in a writing activity involving creating their own lyrics for the tunes reflecting their experiences. Next, for culture education, a teacher asks their students to discuss about, and be aware of, food culture using a specific character's song. Finally, for ethics education, a philosophy of life, natural logic, leadership qualities, and the motto Hakuna Matata("no worries") are explored and discussed through an analysis of the lyrics. The open-ended questionnaire survey is conducted. The result shows that music has a positive effect on culture and ethics education. Film music can be effective in learning a second language, target culture, and ethics.

• Cross-Cultural Studies
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• v.41
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• pp.113-140
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• 2015

This paper explains how the basic and instinctive emotion "joy" is verbally expressed in Russian and Korean. In particular, the main concern of this pater is on the cultural context with which the emotion "joy" is related and the ways in which the emotion "joy" has a wide range of uses. The semantic and pragmatic characteristics of the uses of the expression "joy" can be explained through the cultural and historical backgrounds in both languages. In Russian, joy has two variants, radost' and udovol'stvie. It is very difficult to distinguish a significant difference between them; however, the former is mainly connected with more mental, spiritual, cultural, and religious contexts, whereas the latter is mainly related with more concrete, instantaneous contexts and daily life. The former produces an impression that has a more wide, spiritual, and macroscopic attitude toward a situation, whereas the latter produces an impression that has a microscopic and instantaneous attitude toward a situation. Compared with the Russian expressions, the Korean equivalents, 기쁨 and 즐거움, have a very similar opposition like that of the Russian. The former is based on a more logical and causal relation between an anticipation or desire and the current situations, whereas the latter is based on the participation of speakers in a situation and has a very instantaneous characteristic, like a udovol'stvie in Russian. Thus, it can be reasonable argued that the Russian udovol'stvie and the Korean 즐거움 share many similar semantic properties. In brief summary, in both languages there exists two distinctive variants that show a privative opposition to express the emotional concept of joy.

• 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.