• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.97-106
• /
• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• Journal of the Korean Society of Industry Convergence
• /
• v.27 no.3
• /
• pp.563-572
• /
• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.8
• /
• pp.999-1005
• /
• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.

• Journal of Software Assessment and Valuation
• /
• v.14 no.2
• /
• pp.1-9
• /
• 2018

The proportion of users and companies using open source continues to grow. The size of open source software market is growing rapidly not only in foreign countries but also in Korea. However, compared to the continuous development of open source software, there is little research on open source software subject classification, and the classification system of software is not specified either. At present, the user uses a method of directly inputting or tagging the subject, and there is a misclassification and hassle as a result. Research on open source software classification can also be used as a basis for open source software evaluation, recommendation, and filtering. Therefore, in this study, we propose a method to classify open source software by using machine learning model and propose performance comparison by machine learning model.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.21-37
• /
• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• International Journal of Contents
• /
• v.16 no.3
• /
• pp.101-110
• /
• 2020

Problem solving in real life is a core competency commonly adopted in several subjects recently. The real-world problems were rarely resolved with only one domain of knowledge. Thus, convergence education is becoming a trend in many countries. As on-line education is revitalized because of COVID-19, the need for changes to STEAM education is emerging. In December 2019, the on-line education systems quickly settled in schools because of COVID-19. During the first semester of 2020, most school education was conducted as virtual/distance learning classes. It is necessary to analyze how much technology has been used in the STEAM classes in Korea. This paper analyzes the research papers related to the STEAM education in Korea conducted in the last 10 years. By doing this, we analyze what kind of edutechs we were interested in, what kind of STEAM education actually occurred, and to whom it was conducted. The previous research papers analyzed in this paper are the papers in the Korea Citation Index accredited journals and candidate journals. Finally, this paper proposes educational changing factors to be considered for future technology-based STEAM education.

• Journal of Korea Multimedia Society
• /
• v.22 no.6
• /
• pp.695-705
• /
• 2019

The level of psychological damage caused by verbal abuse among cyberbully victims is very serious. It is going to introduce a system that determines the level of sanctions against chatting in real time using the automatic prohibited words filtering based on artificial neural network. In this paper, we propose a keyword filtering method that detects the modified prohibited words and determines whether the corresponding chat should be sanctioned in real time, and a real-time chatting screening system using it. The accuracy of filtering through machine learning was improved by processing data in advance through coding techniques that express consonants and vowels of similar pronunciation at close distances. After comparing and analyzing Mahalanobis-based clustering algorithms and artificial neural network-based algorithms, algorithms that utilize artificial neural networks showed high performance. If it is applied to Internet chatting, comments or online games, it is expected that it will be able to filter more effectively than the existing filtering method and that this will ease communication inconvenience due to existing indiscriminate filtering methods.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.105-111
• /
• 2022

Multiple-choice survey types have been mainly performed to analyze students' needs for online classes. However, in order to analyze the exact needs of students, unstructured data analysis by answer for essay question is required. Big data is applied in various fields because it is possible to analyze unstructured data. This study aims to investigate and analyze what students want subjects or topics for software lecture in general education that process on non-face-to-face online teaching methods. As for the experimental method, keyword analysis and association analysis of big data were performed with unstructured data by giving a subjective questionnaire to students. By the result, we are able to know the keyword what the students want for software lecture, so it will be an important data for planning and designing software lecture of liberal arts in the future as students can grasp the topics they want to learn.

• Journal of Korean Library and Information Science Society
• /
• v.54 no.3
• /
• pp.223-248
• /
• 2023

This study was conducted to compare and analyze trends in research on reading ability. To do this, collecting articles searched for 'reading ability', a keyword network analysis was performed based on the author's keyword. As a result of the analysis, it was found that the terms were used interchangeably in studies related to reading education at universities. In the study related to reading ability 1, the main research areas are school library, school librarian, information literacy, self-directed learning, and information service. In the study related to reading ability 2, the main research areas are children's early literacy, related reading difficulty and disability. In addition, studies on reading evaluation are being conducted in both areas, but not much activation has been performed. Studies involving adolescents show limited activity in both areas.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.1
• /
• pp.9-17
• /
• 2003

XML(eXtensible Markup Language), next generation internet standard language has the advantage of easy re-use and re-structure in other computing environment because it has the separate data, presentation and structure. In this paper, we implement the efficient retrieval system for the general user by limiting the XML documents on the multimedia learning contents for the virtual education system. The system design is based on SCO Metadata unit defined in SCORM as the proposed virtual education standard. Each XML documents has three indexes - keyword, element and attribute. Also, it makes possible to retrieve data without previous knowledge of the DTD by making the element retrieval screen structure for the user interface. And it gives the user various result screen formats such as XML and HTML by restructuring the retrieval result through XML-QL and XSL, respectively.