• 영어영문학
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• 제55권6호
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• pp.961-973
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• 2009

Walter Benjamin expresses his concern that the new technologies of mechanical reproduction robs the artwork of its own uniqueness, its "aura." Benjamin uses the word "aura" to refer to the sense of awe or reverence one presumably experiences in the presence of works of art. This aura does not merely inhere in the works of art themselves, because Benjamin extends his notion of aura to the level of how he both understands and positions the modern subject in the world of uncertainty and transitoriness. The theoretical framework of Benjaminian aura becomes a crucial and efficient strategic apparatus to read The Education of Henry Adams. As for Benjamin the modern implies a sense of alienation, a historical discontinuity, and a decisive break with tradition, Adams observes that modern civilization has wiped out "tradition," a mythic home in which man can experience order and unity. Adams claims that the growth of science, reason, and multiplicity at the expense of religion, feeling, and unity has been accompanied by a parallel growth in individualism at the expense of community and tradition. To Adams the collapse of traditional values such as maternity, fecundity, and security in America is a waking nightmare of the moral dilemmas of a capitalist society, in which the cruel force of the modern Dynamo is becoming a prime governing principle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• Educational Technology International
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• 제21권2호
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• pp.193-216
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• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• 한국전기전자재료학회논문지
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• 제35권3호
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• pp.246-254
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• 2022

Localized heat can be generated using electrically conductive word-lines built into a 3D NAND flash memory string. The heat anneals the gate dielectric layer and improves the endurance and retention characteristics of memory cells. However, even though the electro-thermal annealing can improve the memory operation, studies to investigate material failures resulting from electro-thermal stress have not been reported yet. In this context, this paper investigated how applying electro-thermal annealing of 3D NAND affected mechanical stability. Hot-spots, which are expected to be mechanically damaged during the electro-thermal annealing, can be determined based on understanding material characteristics such as thermal expansion, thermal conductivity, and electrical conductivity. Finally, several guidelines for improving mechanical stability are provided in terms of bias configuration as well as alternative materials.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.223-231
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• 2022

The application of computer software into the linguistic analysis of texts proves useful to arrive at concise and authentic results from large data texts. Based on this assumption, this paper employs a Computer-Aided Text Analysis (CATA) and a Critical Discourse Analysis (CDA) to explore the manipulative strategies of positive/negative presentation in Orwell's Animal Farm. More specifically, the paper attempts to explore the extent to which CATA software represented by the three variables of Frequency Distribution Analysis (FDA), Content Analysis (CA), and Key Word in Context (KWIC) incorporate with CDA decipher the manipulative purposes beyond positive presentation of selfness and negative presentation of otherness in the selected corpus. The analysis covers some CDA strategies, including justification, false statistics, and competency, for positive self-presentation; and accusation, criticism, and the use of ambiguous words for negative other-presentation. With the application of CATA, some words will be analyzed by showing their frequency distribution analysis as well as their contextual environment in the selected text to expose the extent to which they are employed as strategies of positive/negative presentation in the text under investigation. Findings show that CATA software contributes significantly to the linguistic analysis of large data texts. The paper recommends the use and application of the different CATA software in the stylistic and corpus linguistics studies.

• 여성건강간호학회지
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• 제29권2호
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• 말소리와 음성과학
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• 제15권3호
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• pp.45-52
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• 2023

Previous studies have established a role for cognitive differences in explaining variability in speech processing across individuals. In the case of perceptual cue weighting in the context of a sound change, studies have produced conflicting results regarding the relationship between executive function and the use of redundant cues. The current study aimed to explore this relationship in acoustic cue weighting during speech production. Forty-one Korean-speaking children read a list of stop-initial words and completed two tests that assess executive function, i.e., Dimensional Change Card Sorting (DCCS) and digit n-back. Voice onset time (VOT) and fundamental frequency (F0) were measured in each word, and analyses were carried out to determine the extent to which children's executive function predicted their use of both informative and less informative cues to the three pairs comprising the Korean three-way stop laryngeal contrast. No evidence was found for a relationship between cognitive ability and acoustic cue weighting in production, which is at odds with previous, albeit conflicting, results for speech perception. While this result may be due to the lack of task demands in the production task used here, it nevertheless expands the empirical ground upon which future work in this area may proceed.

• Asia pacific journal of information systems
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• 제30권3호
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• pp.513-531
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• 2020

Online Word-of-Mouth communication, or eWOM, has dramatically changed the way people network, interact, and share knowledge. Studies have examined why consumers choose to share knowledge online, especially online product reviews, as well as the motivations of individuals to share product ideas online. However, the role of gender in shaping the motivation and types of knowledge shared online has been given little consideration. Using concepts from Social Exchange Theory and the Theory of Reasoned Action, we address this research gap by developing and testing a model of gender's influence on knowledge sharing in a social media context. A PLS analysis of survey data from 257 students indicates that reputation, altruism, and subjective norms are key motivators for knowledge sharing intention in social media. More importantly, that gender plays a moderating role within the motivation-knowledge sharing relationship. We also find that subjective norms have a greater impact on knowledge sharing with women than with men. Collectively, our research results highlight individualized factors for improving customer participation in external facing social media for marketing and product innovation.

• Asia pacific journal of information systems
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• 제27권2호
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• pp.77-98
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• 2017

The development of mobile social networking service (SNS) triggers the growth of social commerce industry. Customers rely considerably on electronic word of mouth (eWOM) to make purchasing decisions. Thus, SNS is an important commercial platform that offers attractive opportunities and challenges to firms. This study sheds light on the role of SNS as a social commerce platform by focusing on WeChat, the most popular SNS in China. This study identifies three different types of trust based on SNS that customers perceive in the context of social commerce. These types of trust are contents trust, source trust, and platform trust. This study suggests the antecedents and consequences of each trust. Our results prove that eWOM intention relies on contents trust and source trust, whereas purchase intention depends on contents trust, source trust, and platform trust. This study also finds that contents trust is positively influenced by source trust and platform trust. Finally, the result verifies the key antecedents of each trust, namely, vividness and timeliness for contents trust, competence, benevolence, and integrity for source trust, and instrumental need and social need for platform trust. The discussion and implications on the findings are provided.

• 아태비즈니스연구
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• 제14권4호
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• pp.37-48
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• 2023

Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading. Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis. Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified. Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.