• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.268-272
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• 2023

With the development of SNS, companies and individuals are actively marketing through social media to develop their own products. It is also important to post posts promoting on simple SNS or to show a lot of exposure using algorithms, but customers upload reviews or proof shots of the product on their own, naturally increasing the exposure of the product and increasing the purchasing power of potential customers. As the number of products that users want to purchase through SNS is increasing, they want to access and purchase not only tangible products such as goods and food, but also intangible services through SNS. In this paper, we would like to study exhibitions that have both tangible and intangible characteristics. SNS accounts that mainly introduce these products by searching for reviews have been created while spending leisure time such as exhibitions and fairs, reducing the hassle of searching for personal interests on search engines, and providing prices and reviews from the exhibition's schedule, lowering entry barriers and increasing purchasing power. Using this point, many exhibitions not only display works, but also open various experience centers, and create a photo zone or a unique exhibition hall atmosphere to attract many customers. In this study, we study the impact of SNS on the leisure culture of exhibition. The marketing direction in the situation where SNS marketing is becoming the mainstream is presented, and the change in the form of exhibition is described and presented as an academic approach.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.1-8
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• 2009

Learning user's preference is a key issue in intelligent system such as personalized service. The study on user preference model has adapted simple user preference model, which determines a set of preferred keywords or topic, and weights to each target. In this paper, we recommend multi-perspective user preference model that factors sentiment information in the model. Based on the topicality and sentimental information processed using natural language processing techniques, it learns a user's preference. To handle timc-variant nature of user preference, user preference is calculated by session, short-term and long term. User evaluation is used to validate the effect of user preference teaming and it shows 86.52%, 86.28%, 87.22% of accuracy for topic interest, keyword interest, and keyword favorableness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1571-1576
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• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.2
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• pp.515-523
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• 2017

In the training field where literature is used as a tool, some excerpts from its text are used, instead of its full text. Therefore, it is necessary to have empirical guidelines for which part of the text should be used as Memory-Hint, a part that reminds its reader of certain memory, and for how the text can be introduced effectively. For the study, Hesse's whole life and his literary characters were examined from a therapeutic perspective. First, while Hesse's life was reviewed and his characters were analyzed, Hesse was recognized for Self-therapeutic Life. He also lived a life of multimedia in which he practiced writing, painting, playing musical instruments, meditation, walking, etc. Second, Contents of Literature Therapy using Hesse's works were applied to the schizophrenic patients. Media used for the clinical study were mostly extracted from Hesse's works. They began to show interest in others and express their empathy on others, in addition to expressing their sentimental empathy on Hesse's texts. How effectively Hesse utilized multimedia during his lifetime will be good literary resources in helping improving modern-day people's mental health and curing their pathological problems.

• Journal of English Language & Literature
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• v.57 no.5
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• pp.839-870
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• 2011

This study compares and contrasts Jane Austen's novels of sensibility with those of Rousseau and Goethe. In Julie, or The New Heloise and The Sorrows of Young Werther, the passionate but doomed love of the heroine and her lover is juxtaposed with her passionless marriage to the virtuous husband. In Sense and Sensibility and Pride and Prejudice, Austen revises Rousseau and Goethe's novels of sensibility to accommodate them to the puritanical English literary conventions. She parodies the basic plot of Menage a trois found in their novels of sensibility and transforms her novels into British Bildungsroman, focusing on the heroines' maturation. In Sense and Sensibility, Marianne stands up against the mercenary and snobbish high society. However, Austen represses Marianne's sensibility since the indulgence in sensibility can bring about sexual fall, as is evidenced by the cases of the two Elizas. Marianne's dangerous fever following Willoughby's betrayal emphasizes that female sexual desire should be punished for her continued existence in the high society. The taming of her sensibility and body through the fever is posited as a prerequisite for the happy marriage. In Pride and Prejudice, Elizabeth favors the deprived Wickham over the wealthy Darcy. As Wickham turns out to be a debauched lover, Darcy snatches sexual charms from him and is transfigured into one of the most virtuous and attractive husbands in Menage a trois of the novels of sensibility. Acknowledging sexuality as a vital element of a courtship, Austen embeds sexual desire in dances and glances. However, Elizabeth has to repress sensibility and desire and the complete gratification of desire is continuously deferred to some indefinite period in the future. Marriage is a synecdoche for the union of the bourgeois and the aristocracy in Austen's Bildungsroman and Marianne and Elizabeth are bestowed with happy marriage in return for repressing their sensibility and desire. Since their 'normality' and 'maturation' have been achieved at the expense of subversive sexual power of deviant sensibility, they look too impotent to gratify their desire when they finally secure comfortable but mediocre upper class life.

• Journal of English Language & Literature
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• v.56 no.5
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• pp.797-821
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• 2010

This paper aims to explore how Millay's love sonnets rewrite the carpe diem tradition in the complicated ways. This paper redirects critical attention away from Millay's individual experience and inner self toward the scene of literary history, suggesting that there may be more historical consciousness in Millay's sentimental and feminine "gesture." Rewriting the carpe diem tradition, Millay's sonnets reveal an awareness of the dependence of the carpe diem poems' discursive logic on the woman's coyness, its inability to accomplish its triumph over woman or time (death) without her posited reluctance. Contrary to Andrew Marvel's "To His Coy Mistress," the speakers of Millay's sonnets could never be accused of the sexual coyness; they are outspoken in their defiance of both death and lovers whose possessiveness resembles death's embrace. Moreover, as Stacy Carson Hubbard points out, by converting female sexual experience from its status as a onetime closural event to repeatable one, hence an opportunity for the general and emotional irritability productive of narrative, Millay seizes for the woman the power of "dilation" in both its sexual and its verbal forms. Furthermore, this paper argues that the woman's sex no longer invites analogies to things secret and sealed, preserved or ruined in Millay's sonnets. The woman's promiscuity implies a rejection of monumentalizing love, as well as a refusal of the fixing inherent in the carpe diem's fearful invocation of the movement of time. Throughout the love sonnets, the speaker's sexualized body produces nothing but ephemera. For Millay, this body spends its powers in hopes of having them, and the force of this spending is a perpetual and willful forgetting, which makes possible the repetition of love's story. Ultimately, Milly disturbs our critical categories by rendering permeable boundaries between modern literature and dead form of classic literature, the female speaker and male speaker.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.