• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.469-489
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• 2021

This study explores consumers' responses to socially assistive robotics by using text-mining method focusing on Companion Pets from Hasbro as it gives emotional support. We conducted text frequency analysis, LDA analysis using R programming. The key findings are 1)the most frequently used words the mimicry of living pets and the appearance of companion pets, 2)the five topics were derived from the LDA analysis and classified keywords in each topic split between positive and negative, 3)user, product, environment affect the interaction between consumer and companion pets, 4)consumers who have difficulty in cognition and physical conditions use companion pets to replace living pets. This study provides an understanding of consumer responses in companion pets and gives practical implications that may improve the efficacy of usage for consumers and understand the companion robot, which provides emotional support in COVID-19.

• Korean Educational Research Journal
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• v.42 no.1
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• pp.35-72
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• 2021

This study is to form develops an effective teaching method centered on the Korean "symbol adverb + predicate" type, helping Chinese students to learn Korean to communicate more accurately when expressing detailed complex feelings and various emotions.Manyforeignlanguage learners try to memorize individual words when they acquire the new vocabulary, this may lead to a problematic in that they cannot use Korean vocabulary accurately and naturally because they do not value the combination of vocabulary words. Since symbolic adverbs are not used in isolation and being frequently used with certain vocabulary words, it is more effective to teach them in the form of instruct learners using "symbol adverb + predicate" forms rather than individual vocabulary words. Accordingly, this research considers a particular vocabulary following symbolic adverbs or vocabulary groups with common semantic qualities that could be frequently introduced. Seven Korean language textbooks used by university in domestic Korea and China are compared and analyzed to reveal the aspects of differences in the use of descriptive words after symbolic adverbs. Finally, based on the textbook analysis results, the government propose a plan to improve the Korean "symbol adverb + predicate" type for Chinese learners. However, this study was limit to being unable to present specific educational measures for Chinese learners in the form of "symbol adverb + predicate". This is expected to complement the limitations of this study in subsequent studies, and lead to more specific discussions.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.291-303
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• 2021

In the modern society, the number of people in disaster vulnerable groups is rapidly increasing such as the elderly, the disabled, foreigners, and children. The common characteristics of the groups vulnerable to disasters are that they live in residence types that are exposed to disasters because they are impoverished and if they are exposed to disasters, recovery is a slow process. The purpose of this study is to identify the new risk issues by performing risk issue analysis on the targets of disaster vulnerable group and provide base data for the development of the policies. For the research method, this study centered on the cases of children and pregnant women out of the disaster vulnerable groups and focused on the issue data of social media throughout the past 10 years ('10~'19) and performed social network analysis. As a result, first, the development of the issue showed relevance in the occurrence of specific cases. Second, the awareness about the types, targets, and management method of crisis management was analyzed. Third, an analysis was performed on the sentiment words that considered the solution measures of risk issues or the characteristics of the targets and it was analyzed that there were word that triggered negative emotions. Therefore, it is anticipated for the base data to be used for the government and also for the local government to build an effective crisis management system of the rapidly changing disaster environment on the basis of the sentiment analysis performed on the people of the nation as well as public awareness.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.269-279
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• 2019

The purpose of this study was to investigate if a positive psychological-based music therapy program for gambling addicts' families, causes explicit or implicit attitude change towards gambling addicts. The study focused on families with gambling addicts, who use counseling centers for gambling addiction, or participate in GAM-ANON (GA). The experimental group (n=11) participated in 8 sessions of a positive psychology-based group music therapy program, and the control group (n=8) participated in 8 sessions of personal counseling, or GA family gatherings. To confirm treatment effectiveness, the attitude towards the family relationship, was measured by the explicit (Family Relation Scale) and Implicit Association Task (IAT) methods. Additionally, change in emotions including and anger (PANAS, HBDIS), was measured. Results of the study showed that positive emotions increased significantly, in the positive psychotherapy-based music therapy group compared, to the control group. However, in the treatment group with implicit attitudes, the rate of association of negative words with families accelerated significantly, suggesting that gambling addicts' families may have higher negative emotions. For the future, we discussed the necessity of providing a treatment program, that can directly lead to changes in attitudes of family members of gambling addicts.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.774-787
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• 2021

This thesis is a follow-up study on HwangJeNaeGyeogSoMun(SoMun). Its purpose is the usage of '心' letter used in HwangjenaegyeogYoungChu(YoungChu). The original manuscript of this study was the Hu's Gulin Sanctum of YoungChu. It was conducted by a literature review. Typically, the word '心' means a tangible heart and an intangible mind in the same form. Therefore, in order to understand the contents of the YoungChu, which provides the basis for the basic ideology related to health care, meditation, GiGong training, yoga, practice and oriental medicine, it is necessary to understand the meaning of the word '心' letter. The results of this study are as follows. First, it means human heart. Second, it means the human chest. Third, it means mind such as angry, joy sad, fear and so on. Fourth, it means the transcendent concept like spiritual enlightenment. Fifth, it means the pericardium. Sixth, it means logical thinking. Seventh, it means center or core, Eighth, it means the name of the constellation in the eastern sky of ancient Asia. Ninth, it can be classified into the inside. It can be used as a basic data to understand the contents of YoungChu related to various categories. The limitation of it is that the classification of the '心' letter may be different from the researchers' perspective.

• Journal of the Korean Regional Science Association
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• v.39 no.2
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• pp.47-61
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• 2023

Satisfaction on the residential environment is a major factor influencing the choice of residence and migration, and is directly related to the quality of life in the city. As online services of real estate increases, people's evaluation on the residential environment can be easily checked and it is possible to analyze their satisfaction and its determining factors based on their evaluation. This means that a larger amount of evaluation can be used more efficiently than previously used methods such as surveys. This study analyzed the residential environment reviews of about 30,000 apartment residents collected from 'Zigbang', an online real estate service in Seoul. The apartment review of Zigbang consists of an evaluation grade on a 5-point scale and the evaluation content directly described by the dweller. At first, this study labeled apartment reviews as positive and negative based on the scores of recommended reviews that include comprehensive evaluation about apartment. Next, to classify them automatically, developed a model by using Bidirectional Encoder Representations from Transformers(BERT), a deep learning-based natural language processing model. After that, by using SHapley Additive exPlanation(SHAP), extract word tokens that play an important role in the classification of reviews, to derive determining factors of the evaluation of the residential environment. Furthermore, by analyzing related keywords using Word2Vec, priority considerations for improving satisfaction on the residential environment were suggested. This study is meaningful that suggested a model that automatically classifies satisfaction on the residential environment into positive and negative by using apartment review big data and deep learning, which are qualitative evaluation data of residents, so that it's determining factors were derived. The result of analysis can be used as elementary data for improving the satisfaction on the residential environment, and can be used in the future evaluation of the residential environment near the apartment complex, and the design and evaluation of new complexes and infrastructure.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.