• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.197-217
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• 2018

Music is one of the most creative act that can express human sentiment with sound. Also, since music invoke people's sentiment to get empathized with it easily, it can either encourage or discourage people's sentiment with music what they are listening. Thus, sentiment is the primary factor when it comes to searching or recommending music to people. Regard to the music recommendation system, there are still lack of recommendation systems that are based on customer sentiment. An algorithm's that were used in previous music recommendation systems are mostly user based, for example, user's play history and playlists etc. Based on play history or playlists between multiple users, distance between music were calculated refer to basic information such as genre, singer, beat etc. It can filter out similar music to the users as a recommendation system. However those methodology have limitations like filter bubble. For example, if user listen to rock music only, it would be hard to get hip-hop or R&B music which have similar sentiment as a recommendation. In this study, we have focused on sentiment of music itself, and finally developed methodology of defining new index for music recommendation system. Concretely, we are proposing "SWEMS" index and using this index, we also extracted "Sentiment Pattern" for each music which was used for this research. Using this "SWEMS" index and "Sentiment Pattern", we expect that it can be used for a variety of purposes not only the music recommendation system but also as an algorithm which used for buildup predicting model etc. In this study, we had to develop the music recommendation system based on emotional adjectives which people generally feel when they listening to music. For that reason, it was necessary to collect a large amount of emotional adjectives as we can. Emotional adjectives were collected via previous study which is related to them. Also more emotional adjectives has collected via social metrics and qualitative interview. Finally, we could collect 134 individual adjectives. Through several steps, the collected adjectives were selected as the final 60 adjectives. Based on the final adjectives, music survey has taken as each item to evaluated the sentiment of a song. Surveys were taken by expert panels who like to listen to music. During the survey, all survey questions were based on emotional adjectives, no other information were collected. The music which evaluated from the previous step is divided into popular and unpopular songs, and the most relevant variables were derived from the popularity of music. The derived variables were reclassified through factor analysis and assigned a weight to the adjectives which belongs to the factor. We define the extracted factors as "SWEMS" index, which describes sentiment score of music in numeric value. In this study, we attempted to apply Case Based Reasoning method to implement an algorithm. Compare to other methodology, we used Case Based Reasoning because it shows similar problem solving method as what human do. Using "SWEMS" index of each music, an algorithm will be implemented based on the Euclidean distance to recommend a song similar to the emotion value which given by the factor for each music. Also, using "SWEMS" index, we can also draw "Sentiment Pattern" for each song. In this study, we found that the song which gives a similar emotion shows similar "Sentiment Pattern" each other. Through "Sentiment Pattern", we could also suggest a new group of music, which is different from the previous format of genre. This research would help people to quantify qualitative data. Also the algorithms can be used to quantify the content itself, which would help users to search the similar content more quickly.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.89-115
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• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.113-123
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• 2018

The purpose of this research is to develop evaluation indicators forperformance improvement of horticultural therapy garden. In order to achieve a therapeutic purpose, the gardening activity held by the trained horticultural therapist. Moreover, horticultural therapy is 'a medical model' for the treatment and basic premise of the research was set, as horticultural therapy garden is characterized area to support activities of patients and horticultural therapist functionally and efficiently. For this study, three times of Delphi and AHP techniques were proceeded to export panels who were recruited by purposive sampling. Through these techniques, it was possible to deduct the evaluation indicator which maximizes the performance of the horticultural therapy garden. The evaluation items were prioritized by typing and stratification of the indicator. The results and discussions were stated as followings. Firstly, a questionnaire of experts was conducted to horticultural therapists and civil servants who were in charge of horticultural therapy. As results(horticultural therapists: 87.8%, civil servants: 75.2%), It is possible to conclude that both positions have the high recognition and agreed on the necessity of horticultural therapy. Secondly, Delphi investigation was conducted three times in order to develop the evaluation indicator for performance evaluation. After Delphi analysis, total 34 of evaluation elements to improve the performance of the horticultural therapy garden by reliability and validity analysis results. Thirdly, AHP analysis of each evaluation indicator was conducted on the relative importance and weighting. Moreover, the results showed 'interaction between nature and human' as the most important element, and in order of 'plan of the program', 'social interaction', 'sustainable environmental', and 'universal design rule', respectively. On the other hand, the exports from the university and research institute evaluated the importance of 'interaction between nature and human', while horticultural therapists chose 'plan of the program' as the most important element. Fourthly, the total weight was used to develop weight applied evaluation indicator for the performance evaluation of the horticultural therapy garden. The weight applying to evaluation index is generally calculated multiply the evaluation scores and the total weight using AHP analysis. Finally, 'the evaluation indicator and evaluation score sheet for performance improvement of the horticultural therapy garden' was finally stated based on the relative order of priority between evaluation indicators and analyzing the weight. If it was deducted the improvement points for the efficiency of already established horticultural therapy garden using the 'weight applied evaluation sheet', it is possible to expand it by judging the importance with the decision of the priority because the item importance decided by experts was reflected. Moreover, in the condition of new garden establishment, it is expected to be helpful in suggesting ways for performance improvement and in setting the guidelines by understanding the major indicators of performance improvement in horticultural therapy activity.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• 한국노년학
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• v.39 no.4
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• pp.921-939
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• 2019

According to the social selectivity theory (SST), despite the disadvantages of life conditions, older adults experience less negative emotions because they regulate their emotions by avoiding negative stimuli or situations. Based on the SST, this study attempted to find out whether older adults are better able to regulate negative emotions than young adults in the Ultimatum Game (UG). In an UG, if the proposer proposes to distribute a portion of the money to the responder, the responder must decide whether to accept or reject it. If the responder accepts the offer, the proposer and the responder can each have their own share as proposed, but if s/he reject the offer, both get nothing. Thus, if the responder considers own economic benefits, it is a more reasonable decision to accept the unfair offer no matter how low, than to reject it. To accept an unfair offer, the responder must regulate the anger felt at the proposer. If older adults could regulate anger better than young adults, they would be less likely to reject the unfair offer than young adults. Fifty-seven olders and 60 university students participated in this study. Both the older and young adults accepted most of the fair offers. In contrast, older adults accepted unfair offers at a significantly higher rate than young adults. In addition, compared to young adults, older adults reported anger less frequently at the unfair offers. Accepting unfair offers was negatively correlated with anger report, but positively correlated with the emotion regulation measured by ERQ. The ERQ score was negatively correlated with anger report. Emotion regulation partially mediated the relationship between the age groups and acceptance of unfair offers. The present results showed that older adults accepted the unfair offers at a higher rate than young adults because they could regulate the negative emotions felt at the unfair offer better than young adults. This study provided new evidence for the claim that improving emotional regulation is a major developmental change in adulthood.

• Journal of Ginseng Research
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• v.48 no.5
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• pp.504-510
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• 2024

Background: As a physiological function of body, immunity can maintain health by identifying itself and excluding others. With economic development and increasingly fierce social competition, the number of sub-healthy population is gradually increasing, and the most basic problem exposed is human hypoimmunity. Hypoimmunity can be manifested as often feeling tired, catching colds, mental depression, etc. In order to enhance immunity, eating healthy foods with the effect of enhancing immunity may become an effective choice. KRG has pharmacological effects of enhancing immunity. Because the screening and evaluation method of immune population are not unified, there are relatively few KRG immunity tests for sub-health population. It is of great significance to study the effect of KRG on people with hypoimmunity to improve sub-health status. Methods: This was a 180-day, randomized, double-blind, placebo-controlled clinical trial. According to the trial scheme design, 119 qualified subjects were included and randomly divided into the test group taking KRG and the placebo control group. Subjects need to check safety indicators (blood pressure and heart rate, blood routine, liver and kidney function, urine routine and stool routine) and efficacy indicators (main and secondary) inspection at baseline, efficacy indicators inspection during the mid-term of the test (90th days of administration), safety and efficacy indicators inspection after the test (180th days of administration). Results: After the test, the safety indicators of placebo control group and KRG test group were basically within the normal range, and there is no significant difference in fireness score between the two groups. Through follow-up interviews, it was found that the subjects in the test group and the control group had no adverse reactions and allergic reactions such as nausea, flatulence, diarrhea, and abdominal pain during the test period. Self-comparison of the test group, the results of the main efficacy indicators: (1) immune related health scores were significantly improved in the mid-term and after the test (P < 0.01), (2) CD3 and CD4/CD8 increased significantly after the test (P < 0.05), (3) IgG, IgA, IgM and WBC increased significantly in the mid-term and after the test (P < 0.01); the results of the secondary efficacy indicators: (1) TNF-α decreased significantly in the midterm (P < 0.05), IFN-γ decreased significantly in the mid-term (P < 0.01), (2) NK increased significantly in the mid-term and after the test (P < 0.05), (3) monocyte increased significantly in the mid-term and after the test (P < 0.01). Inter-group comparison of the test group and the control group, the results of the main efficacy indicators: (1) immune related health scores were higher than that of the control group in the mid-term and after the test (P < 0.01), (2) IgA of the test group was higher than that of the control group in the mid-term and after the test (P < 0.05); the results of the secondary efficacy indicators: (1) WBC of the test group was higher than that of the control group in the mid-term (P < 0.05); (2) monocytes of the test group were higher than that of the control group in the mid-term and after the test (P < 0.05), neutrophils of the test group were higher than that of the control group in the mid-term (P < 0.05). Conclusion: Taking KRG has no adverse effects on the health of the subjects. According to the standard of clinical trial scheme, the immune related health scores and IgA in the main efficacy indicators were positive, which shows that KRG is helpful in enhancing human immunity.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.