• Journal of Digital Convergence
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• v.15 no.9
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• pp.87-97
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• 2017

This paper examined how consumers' attitude toward the telecommunication expense was affected by news reports through the experiment. Participants were randomly assigned to one of four group (control group, high-expense-claimed-media group, low-expense-claimed-media group, and both-exposed-media group), and asked to indicate credibility & neutrality toward media report, similarity between media report, and their own thought, their attitude toward the telecommunication expense. The result of ANOVA showed that the high-expense-claimed-media was perceived more credible and neutral than the low-expense-claimed-media. ANCOVA was conducted to eliminate the impact of similarity between media report and their own thought on the evaluation of credibility & neutrality toward media report, and the result showed that there was no difference. Also, participants evaluated the telecommunication service so expensive, regardless of what kind of media reports they were exposed. We found that consumers' prior belief, which telecommunication service was expensive, might interrupt consumers' learning process for new information from media. To resolve the social pressure about mobile service rate-cutting, it is necessary to investigate how to dampen consumers' stereotype about the telecommunication expense. The future research using the framing effect could be considerable.

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

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.926-932
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• 2009

Recently many research reports on effective use of mobile devices for museums to provide information on displayed artifacts providing individualized learning space, collaborative learning, and discovery learning, Such devices have many possibilities to support learning as a participatory media and social network. Mobile devices are used, however, limited for its usability and lack in providing expected learning experiences. It offers one-way interaction and they are often limited in providing customized services for different patrons to experience learning and entertainment. In this research, we have adopted user centered design approach to identify the needs and possible usage of PDA system in the museum. Research methods include contextual observation and inquiry with symbolic interactionism for qualitative research and its epistemology. We have developed conceptual model with scenario and storyboard method, and developed vertical prototype with Flash.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.148-158
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• 2024

We investigate Autism Spectrum Disorder (ASD), which is typified by deficits in social interaction, repetitive behaviors, limited vocabulary, and cognitive delays. Traditional diagnostic methodologies, reliant on expert evaluations, frequently result in deferred detection and intervention, particularly in South Korea, where there is a dearth of qualified professionals and limited public awareness. In this study, we employ advanced deep learning algorithms to enhance early ASD screening through automated video analysis. Utilizing architectures such as Convolutional Long Short-Term Memory (ConvLSTM), Long-term Recurrent Convolutional Network (LRCN), and Convolutional Neural Networks with Gated Recurrent Units (CNN+GRU), we analyze video data from platforms like YouTube and TikTok to identify stereotypic behaviors (arm flapping, head banging, spinning). Our results indicate that the LRCN model exhibited superior performance with 79.61% accuracy on the augmented platform video dataset and 79.37% on the original SSBD dataset. The ConvLSTM and CNN+GRU models also achieved higher accuracy than the original SSBD dataset. Through this research, we underscore AI's potential in early ASD detection by automating the identification of stereotypic behaviors, thereby enabling timely intervention. We also emphasize the significance of utilizing expanded datasets from social media platform videos in augmenting model accuracy and robustness, thus paving the way for more accessible diagnostic methods.

• Archives of design research
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• v.18 no.2 s.60
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• pp.79-90
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• 2005

The aim of this study was to investigate how the psychological factors of human interrelation or human socialization are associated with the visual design based on sociological and psychological theories. To accomplish this goal, human socialization was examined on the basis of physiology, philosophy, and psychology. Then a case study was employed to assess how they function in the area of visual design. In literature, the sources of psychological factors of human socialization were categorized into 11 items including the sexual hedonic pursuit. These items were used for the evaluation of 40 printed media advertisements, all of which were the winners of the Korea Advertising Awards from 1994 to 2003. As a result, it was revealed that most advertisements responded to the items of adaptive value and cultural imprinting as biological bases. Also, it was discovered that the existential foundation of advertising has been based on mutual distrust and the payoff matrix as a mind of social unrest. In conclusions, it was illustrated that future advertising will remain based on adaptive value, cultural imprinting, social learning, and imitation learning, as long as advertising continue to hold its reason for existence.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.1-13
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• 2019

This study examined the effect of digital technologies on adolescent mental health; attention deficit, aggression, depression. Furthermore, we investigated the role of parenting style and peer attachment as they might have effects on digital technologies. Using two-wave longitudinal data, we conducted longitudinal analysis from the Korean Children and Youth Panel Survey done in 2015 and 2016. Results showed that computer and internet usage for learning purpose has a positive effect on mental health, while computer games and social media have negative effects. Also, the positive effects of learning on depression indicated stronger in adolescents who felt less affection from their parents. On the other hand, the negative effects of computer games and social media could be moderated by both parenting style and peer attachment. Implications of these results and directions for future research are discussed.

• Asian review of World Histories
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• v.4 no.1
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• pp.125-132
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• 2016

This paper, as a conlcusion to this special issue, presents the future work that is being carried out at NTU Singapore in collaboration with Microsoft Research and Microsoft Azure for Research. For our research team the real frontier research in world histories starts when we want to use computers to structure historical information, model historical narratives, simulate theoretical large scale hypotheses, and incent world historians to use virtual assistants and/or engage them in teamwork using social media and/or seduce them with immersive spaces to provide new learning and sharing environments, in which new things can emerge and happen: "You do not know which will be the next idea. Just repeating the same things is not enough" (Carlo Rubbia, 1984 Nobel Price in Physics, at Nanyang Technological University on January 19, 2016).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.161-166
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• 2014

Using and increasing the smart devices got many changes for social environment. Especially, many researchers have studied application using smart devices for improving the learning performance in education. However, Liberal Arts of Humanities has very difficult to apply to information and communication technology cause of traditional study pattern and unit characteristic. In this paper, we suggest a method to apply the Liberal Arts of Humanities to digital broadcasting and to improve learning performance. For this study, we propose a method to making smart contents and connecting them for Liberal Arts of Humanities. And also, we propose a method for applying them to digital broadcasting in order to proof the learning performance increasing by questionary, and shows the application possibility.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.400-420
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• 2024

Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia's ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia's bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.