• Smart Media Journal
• /
• v.5 no.1
• /
• pp.61-68
• /
• 2016

According to increasing SNS users and developing smart devices like smart phone and tablet PC recently, many techniques to classify user emotions with social network information are researching briskly. The use emotion classification stands for distinguishing its emotion with text and images listed on his/her SNS. This paper suggests a method to classify user emotions through sampling a value of a representative figure on a trigonometrical function, a representative adjective on text, and a canny algorithm on images. The sampling representative adjective on text is selected as one of high frequency in the samplings and measured values of positive-negative by SentiWordNet. Figures sampled on images are selected as the representative in figures; triangle, quadrangle, and circle as well as classified user emotions by measuring pleasure-unpleased values as a type of figures and inclines. Finally, this is re-defined as x-y graph that represents pleasure-unpleased and positive-negative values with wheel of emotions by Plutchik. Also, we are anticipating for applying user-customized service through classifying user emotions on wheel of emotions by Plutchik that is redefined the representative adjectives and figures.

• KIISE Transactions on Computing Practices
• /
• v.21 no.11
• /
• pp.733-738
• /
• 2015

User demographic information is necessary in order to improve the quality of personalized services such as recommendation systems. Mobile data, especially text data, is known to be effective for prediction of user demographic information. However, mobile text data has privacy issues so that its utilization is limited. In this regard, we introduce an on-device gender prediction framework utilizing mobile text data while minimizing the privacy issue. Discriminative word and emoticon sets of each gender are constructed from web documents written by authors of each gender. After gender prediction is performed by comparing discriminative word and emoticon sets with a user's mobile text data, an ensemble method that combines two prediction results draws a final result. From experiments conducted on real-world mobile text data, the proposed on-device framework shows promising results for gender prediction.

• Cross-Cultural Studies
• /
• v.45
• /
• pp.55-83
• /
• 2016

To provide a set of data with which instructors may boost the sagging demand for Anglo-American poetry classes, this thesis classifies the kinds of difficulties the students face in reading English poems. Asses to the classification is an analysis on the causes of the difficulties at different levels of the reading process, from the linguistic to the cultural. Arnoldian insight argues that poetry is the best of all forms of writing. Without an ample exposure to poetry, average English majors would barely sharpen the skills that they use to deal with other kinds of writing. To help ease the continuing need for a workable teaching model in English poetry reading classes, this thesis suggests focusing on the kinds of wrong translations produced by the students. According to the theory of cultural translation, any translation, even the wrong kind, is already a product of a very complicated process of interpretation that involves many cultural factors. With the analysis of these factors discovered in Korean college English reading classes, this thesis tries to explain the mechanisms through which wrong translations are produced, since these inevitably lead to wrong interpretations of given poetic texts.

• Trans-
• /
• v.13
• /
• pp.141-169
• /
• 2022

Analysis of narrative texts has been regarded as academically and practically important, and has been made from various perspectives and methods. In this paper, the computational narrative analysis methodology from the perspective of information processing was examined. From the point of view of information processing, the creation and acceptance of narrative is a bidirectional coding process mediated by narrative text, and narrative text can be said to be a multi-layered structured code. In this paper, four methodologies that share this point of view - character network analysis, text mining and sentiment analysis, continuity analysis of event composition, and knowledge analysis of narrative agents - were examined together with cases. Through this, the mechanism and possibility of computational methodology in narrative analysis were confirmed. In conclusion, the significance and side effects of computational narrative analysis were examined, and the necessity of designing a human-computer collaboration model based on the consilience of the humanities and science/technology was discussed. Based on this model, it was argued that aesthetically creative, ethically good, politically progressive, and cognitively sophisticated narratives could be made more effectively.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.4
• /
• pp.174-180
• /
• 2024

In this paper, we explore an emotion classification method through multimodal learning utilizing wav2vec 2.0 and KcELECTRA models. It is known that multimodal learning, which leverages both speech and text data, can significantly enhance emotion classification performance compared to methods that solely rely on speech data. Our study conducts a comparative analysis of BERT and its derivative models, known for their superior performance in the field of natural language processing, to select the optimal model for effective feature extraction from text data for use as the text processing model. The results confirm that the KcELECTRA model exhibits outstanding performance in emotion classification tasks. Furthermore, experiments using datasets made available by AI-Hub demonstrate that the inclusion of text data enables achieving superior performance with less data than when using speech data alone. The experiments show that the use of the KcELECTRA model achieved the highest accuracy of 96.57%. This indicates that multimodal learning can offer meaningful performance improvements in complex natural language processing tasks such as emotion classification.

• Journal of Digital Convergence
• /
• v.20 no.4
• /
• pp.133-145
• /
• 2022

As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.

• Proceedings of the Korea Contents Association Conference
• /
• 2010.05a
• /
• pp.82-84
• /
• 2010

그동안 커뮤니케이션 효과에 관한 많은 연구들이 진행되어 왔다. 특히 의제설정 관련 이론들은 맥콤과 쇼(McCombs & Shaw, 1972)[1]에 의해 최초의 연구가 발표된 이후, 수백 편 이상의 관련 연구들이 축적되어 왔다. 이러한 연구들을 통해서 미디어가 중요하게 보도한 의제는 수용자 의제에 많은 영향을 미친다는 사실을 누구도 부정하기 어렵다. 그러나 이러한 연구들은 주로 언어텍스트를 중심으로 이루어져왔다. 그러나 신문이나 인터넷 미디어와는 달리, TV 미디어에서 수용자들은 언어텍스트에 의해서만 정보를 얻는다고 보기는 어렵다. 본 연구는 텔레비전 뉴스 보도에서 영상과 자막이 수용자의 의제설정 효과에 어떤 영향을 미치는지 실험을 통해 검증해보고자 한다.

• Journal of Korean Society of Archives and Records Management
• /
• v.24 no.1
• /
• pp.89-109
• /
• 2024

The study analyzed the research trends of domestic and international record information services from 2003 to 2022. A total of 136 academic papers registered in the Korea Citation Index (KCI) and 74 from the Library, Information Science & Technology Abstracts (LISTA) were examined by quantitative and qualitative content analysis to understand the research status of 20 years from various angles, such as publication year, research type, researcher type, subject, and purpose. Frequency analysis, co-occurrence frequency analysis, centrality analysis, and topic modeling were performed by applying text mining techniques. Results showed that domestic papers demonstrated a research flow focused on specific institutions or records, and user-centered satisfaction surveys and content-centered studies were conducted. Moreover, foreign papers confirmed various evaluation-oriented and information provision studies, such as data, resources, and collections, along with the research trend focusing on the relationship between archivists and users. The management of information resources was identified as a common topic in both domestic and foreign papers, but it is possible to identify that domestic research focuses on maintaining the quality of domestic information resources, while foreign research focuses on the storage and retrieval of information.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.28 no.1
• /
• pp.191-207
• /
• 2017

This paper employs entitymetrics analysis on the research works of Dong-ju Yun. He was a Korean poet who was studied by many researchers on his works, religion and life. We collected 1,076 papers about Dong-ju Yun and conducted various approaches including co-author citation analysis, topic modeling analysis to identify the topic trend in the study of Dong-ju Yun. Also we extracted entities like person's name and literature's title from abstract to examine the relationship among them. The result of this paper enables us to objectively identify the topic trend and infer implicit relationships between key concept associated with Dong-ju Yun based on text data. Moreover, we observed sub-research topics such as life, poem, aesthetic existence, comparative literature, literary translation, and religious beliefs. This paper shows how entitymetrics can be utilized to study intellectual structures in the humanities.

• Journal of Technology Innovation
• /
• v.24 no.4
• /
• pp.249-275
• /
• 2016

For the past 50 years, innovation field has gone through an evolution. The range of research topics on innovation has expanded and diversified, along with a quantitative increase. In a multi-disciplinary field like innovation, to explore new topics and understand research trends, it is necessary to possess a comprehensive understanding regarding the current status of, and trends in, the research. In this study, the research trend in innovation studies from 2000 to 2015 was analyzed in a holistic perspective. For this, a novel technique, text mining was used. The result shows that innovation studies has focused on the traditional and emerging topics. Also, the differentiations has appeared in some of the traditional topics. This study provides not only an understanding of research dynamics, but also an opportunity to gain insights into the evolution of a new paradigm from an academic perspective.