• English & American cultural studies
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• v.10 no.2
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• pp.209-230
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• 2010

This paper reads Tristram Shandy around the issue of hobbyhorse, Sterne's main contribution to novelistic techniques as well as his insightful understanding of the modern condition. First, Sterne represents his characters according to the principle of hobbyhorse, declaring "I will draw my uncle Toby's character from his HOBBY-HORSE." Gradually distancing himself from the Juvenalian satiric mode as well as Henry Fielding's grand narrative and Samuel Richardson's psychological realism, as is seen in the early episode of Yorick's death, Sterne suggests that the best way to represent his characters lies in describing their hobbyhorses. Sterne's foregrounding of hobbyhorse is linked with his embrace of madness as part of the modern identity. He accepts that hobbyhorse-riding, a quirky and mad habit of mind or behavior, is indispensable for some people, like Uncle Toby, to survive and get along with their otherwise unbearable lives. Uncle Toby's hobbyhorse of waging mock battles in the bowling green saves him from the perplexing real world of language and sexuality, while the fictionality of his hobbyhorsical world is exposed by Widow Wadman. Since a hobbyhorse is by definition a world of private pleasure and eccentricity, sentimentalism comes along to bridge the two virtually incommensurable hobbyhorsical world in place of linguistic communication. Yet if Tristram Shandy fully stages sentimentalism, a cardinal part of hobbyhorse riding, it also offers an awareness of it, which is a significant development in the cult of sentimentalism in the eighteenth century. Tristram Shandy performs a version of sentimental journey where each character rides his hobbyhorse and the reader is invited to ride his/her own hobbyhorse.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.142-151
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• 2022

In this paper, a model combined with explanatory artificial intelligence (xAI) models was presented to secure the reliability of machine learning-based sentiment analysis and prediction. The applicability of the proposed model was tested and described using the IMDB dataset. This approach has an advantage in that it can explain how the data affects the prediction results of the model from various perspectives. In various applications of sentiment analysis such as recommendation system, emotion analysis through facial expression recognition, and opinion analysis, it is possible to gain trust from users of the system by presenting more specific and evidence-based analysis results to users.

• Information Systems Review
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• v.22 no.4
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• pp.59-74
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• 2020

This paper investigates the asymmetric effect of social sentimental on an individual stock price return. For this purpose, four companies such as POSCO, Korean Electricity, AMORE PACIFIC, KIA Motors are chosen from KOSPI listed companies in terms of dataperspective. The main estimation results are as follows: the positive opinions affect only the stock prices return of three companies while the negative opinions affect all of the companies. It shows that positive or negative texts give asymmetric effect on stock price return and the effect of negative opinions is bigger than that of positive opinions. The results imply that investors are more sensitive to the negatives since they have the tendency of loss aversion. Also, it indicates that subjective opinion on SNS can be used as the proxy for the investment sentiment.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.117-128
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• 2017

Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data. Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, $na{\ddot{i}}ve$ Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data. Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components. Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data. Word2vec method also shows comparative results to discover the relevance of components precisely.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.49-62
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• 2022

Online customer review data can be easily collected on the Internet and also they describe sentimental evaluation of a product in different aspects. Previous sentiment analysis studies evaluate the degree of sentiment with review data, which may have multiple sentences describing different product aspects. Since different aspects of a product can be described in a sentence, the proposed method suggested analyzing a sentence to build a pair of a product aspect terms and sentimental terms. Bidirectional LSTM and CRF algorithms were used in this paper. A pair of aspect terms and sentimental terms are evaluated by pre-defined evaluation rules. The paper suggested using the result of evaulation as inputs of QFD, so that the quantified customer voices effect on the requirements of a new product. Online reviews for a hair dryer were used as an example showing that the proposed approach can derive reasonable sentiment analysis results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.766-768
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• 2010

매체의 발달은 타이포그래피에 있어서 키네틱 타이포그래피의 활용을 통해 감정이 전달이 풍부해 질 수 있도록 해 주었지만 그 기본 근간이 되는 프레임의 면모를 살펴보면 지면이나 화면 한 페이지에서도 볼드나 행간의 간격, 폰트의 사용, 여백 등의 요소뿐 아니라 실험적 타이포그래피를 통해 감정을 표현할 수 있다는 것을 알 수 있다.

• Journal of the Korean Society of Costume
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• v.57 no.6 s.115
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• pp.71-86
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• 2007

Affinity between a creative and experimental fashion design and a Fashion Make-up expressed in John Galliano's Collection was analysed to examine the harmony between Beauty and Fashion. This approach may establish the link between the Fashion Make-up analysed in view of Aesthetics and aesthetic characteristics of a fashion design that a fashion designer pursues. The Fashion Make-up plays a significant role to express a relevance to a design spirit because it is a visual text that a audience faces easily in a collection leading the style. Under the proposition that collection is understood as a popular culture as the fashion is preferential and popular, the Fashion Make-up can be analysed in the aspect of aesthetics. The characteristics reflecting the popularity of popular culture, such as the comic, the erotic, the fantastic and the sentimental are used to analyse and interpret the Fashion Make-up. The fashion design and Fashion Make-up with one characteristics or combined ones showing uniqueness in the popular culture are compared and analyzed.

• The Research Journal of the Costume Culture
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• v.18 no.3
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• pp.408-422
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• 2010

The purposes of this study is to investigate the attitudes and value of fashion product description and consumer replies used in online shopping malls, and to examine the differences in the perceived reliability(objectivity, expertise, trustworthiness) preference and purchase intention toward the product as determined by the appeal type (evaluation-sentimental vs. factual-information) of the product description, the direction(negative vs. positive) and type(factual vs. evaluative) of consumer replies for the product in online shopping malls. Data was collected from female college students with fashion products purchase experience at online shopping malls by questionnaire survey (N=424) and analyzed by using frequency analysis, t-test and ANOVA. Results showed that consumers respondents tended to read product description and other consumer replies before purchasing, when shopping for fashion products through an on-line shopping mall. They thought that sellers' product description and the consumers' replies were helpful in making their decision; but, they were also skeptical about product description. Respondents showed higher perceived reliability, preference and purchase intention to the factual-information type product description than the evaluation-sentimental type. Positive consumer replies were more effective in yielding higher preferences and purchase intentions. Factual replies tend to yield higher reliability than evaluative replies.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1184-1191
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• 2021

In a pop song, the creator's intention is communicated to the user through music and lyrics. Lyric meaning is as important as music, but in most cases lyrics are delivered to users in a static form without non-verbal cues. Providing lyrics in a static text format is inefficient in conveying the emotions of a music. Recently, lyrics video with kinetic typography are increasingly provided, but producing them requires expertise and a lot of time. Therefore, in this system, the emotions of the lyrics are found through the analysis of the text of the lyrics, and the deep learning model is trained with the data obtained by converting the melody into a Mel-spectrogram format to find the appropriate emotions for the music. It sets properties such as motion, font, and color using the emotions found in the music, and automatically creates a kinetic typography video. In this study, we tried to enhance the effect of conveying the meaning of music through this system.