• Asia pacific journal of information systems
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• v.33 no.4
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• pp.999-1015
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• 2023

Smart speakers represent a growing product in home electronics. However, their capability to record voices in their immediate surroundings has spurred concerns about privacy violations. In this paper, we assess the extent of those concerns in the opinions of smart speaker users by examining the reviews posted by smart speaker users. We focus on South Korea as a representative of advanced Asian economies. The results show that Korean smart speaker users are either unconcerned or unaware of privacy issues, confirming the results of previous studies about UK users, but with an even lower degree of interest in the topic. However, for the few users concerned about privacy, their attitude towards privacy influences their overall opinion about smart speakers.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.110-118
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• 2020

Social media data serves as a proxy indicator to understand the problems and the future of public opinion in Korean society. This research used 109,015 news data from 2016 to 2018 to analyze the sensitivity of the elderly and employment in Korean society, and explored the possibility of expanding the labor force in Korean society, which is facing a cliff between the elderly and the population. Topic keywords for employment of the elderly include "elderly*employment", "elderly*employment", and "elderly*wage". As a result of the analysis, positive sensitivity prevails for most of the period, and it is possible to expand the working-age population. Positive feelings about expanding employment opportunities for the elderly and negative feelings about low wages have brought to light the reality of the elderly who are still poor despite their work. In this study, social big data was used to analyze the perceptions and sensibilities of Korean society related to the elderly and employment through hierarchical crowd analysis and related text mining analysis.

• The Research Journal of the Costume Culture
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• v.28 no.2
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• pp.265-284
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• 2020

This study aimed to analyze the performance of Disney-collaborated fashion lines based on online consumer reviews. To do so, the researchers employed text mining and network analysis to identify key words in the reviews of these products. Blogs, internet cafes, and web documents provided by Naver, Daum, and YoutTube were selected as subjects for the analysis. The analysis period was limited to one year after for the 2019. Data collection and analysis were conducted using Python 3.7, Textom, and NodeXL. The research terms in question were as follows: 'Disney fashion collaboration' and 'Frozen fashion collaboration'. Preliminary survey results indicated that 'Elsa's dress' was the most frequently mentioned term and that the domestic fashion brand Eland Retail was the most active in selling Disney branded clothing through its own brand. The writers of reviews for Disney-collaborated fashion products were primarily mothers with daughters. Their decision to purchase these products was based upon the following factors; price, size, stability of decoration, shipping, laundry, and retailer. The motives for purchasing the product were the positive response of the consumer's child and the satisfaction of the parents due to the child's response. The problems to be solved included insufficient quantity of supply, delay in delivery, expensive price considering the number of times children's clothes are worn, poor glitter decoration, faded color, contamination from laundry, and undesirable smells immediately after the purchase.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.260-267
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• 2016

The Internet has brought lots of changes to us sharing information mutually. However, as all social symptom have double-sided character, it has serious social problem. Vicious users have been taking advantage of anonymity on the Internet, stating comments aggressively for defamation, personal attacks, privacy violation and more. Malicious comments on the Internet are creating the biggest problem regarding unlawful acts and insults which occur on the Internet. In order to solve the issues, several studies have been done to efficiently manage the comments. However, there are limitations to recognize modified malicious vocabulary in previous research. So, in this paper, we propose a malicious comments detection technique by improving limitation of previous studies. The experimental result has shown accuracy of 87.8% providing higher accuracy as compared to previous studies done.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• Trans-
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• v.13
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• pp.141-169
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• 2022

Analysis of narrative texts has been regarded as academically and practically important, and has been made from various perspectives and methods. In this paper, the computational narrative analysis methodology from the perspective of information processing was examined. From the point of view of information processing, the creation and acceptance of narrative is a bidirectional coding process mediated by narrative text, and narrative text can be said to be a multi-layered structured code. In this paper, four methodologies that share this point of view - character network analysis, text mining and sentiment analysis, continuity analysis of event composition, and knowledge analysis of narrative agents - were examined together with cases. Through this, the mechanism and possibility of computational methodology in narrative analysis were confirmed. In conclusion, the significance and side effects of computational narrative analysis were examined, and the necessity of designing a human-computer collaboration model based on the consilience of the humanities and science/technology was discussed. Based on this model, it was argued that aesthetically creative, ethically good, politically progressive, and cognitively sophisticated narratives could be made more effectively.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.15-36
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• 2021

The airline industry faces with significant competition due to the rise of technology innovation and diversified customer needs. Therefore, continuous quality management is essential to gain competitive advantages. For this reason, there have been various studies to measure and manage service quality using customer reviews. However, previous studies have focused on measuring customer satisfaction only, neglecting systematic management between customer expectations and perception based on customer reviews. In response, this study suggests a framework to identify relevant criteria for service quality management, measure the importance, and assess the customer perception based on customer reviews. Machine learning techniques, topic models, and sentiment analysis are used for this study. This study can be used as an important strategic tool for evaluating service quality by identifying important factors for airline customer satisfaction while presenting a framework for identifying each airline's current service level.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.102-108
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• 2023

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.