• 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 the Daesoon Academy of Sciences
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• v.27
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• pp.245-282
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• 2016

Currently, the whole world is being swept away by spiritual movements seeking divinity in oneself. Yet there are terror attacks, religious disputes and other conflicts continuously taking place on larger and larger scales as well as expanding further and further throughout the world. Interreligious harmony seems like a distant ideal. What is the ultimate cause of religious conflicts? Is interreligious communication truly that difficult? Even among different cultures, said cultures' varieties of ritual expressions, and various religious doctrines, there are points of general common to be appreciated if a deep perspective is adopted. When we find the common ground and understand each other's difference, it will be easier to communicate since everyone will be learning from each other. What could serve as common ground for different religions? Many scholars speak about the state of 'oneness' that is claimed by mysticism throughout a large array of religions. This state of oneness is typically not achieved overnight, but it serves as a prospective state which is pluralistically inclusive. This "religion of enlightenment" emphasizes the process of reaching comprehensive interreligious agreement would be characterized by a deep religious perspective. If superficial religious perspectives focuses only on faith to attain blessings and engage in blind belief, then, by contrast, deep religious perspectives emphasize inner divinity, the true self, orthe higher self. The words, 'superficial religious perspective' and 'deep religious perspective' were defined for personal convenience by O Gang-nam, a scholar of comparative religion. Consequently, this classification is a relative binary concept lacking hard and fast rules with regards to distinctions. But the concept of superficial religious perspectives and deep religious perspectives has its advantage in allowing clearer and easier discussion about religions because it could embrace all aspects of religious life and the development of various religious sentiment. In this way, the terms surface religious perspectives and deep religious perspectives will be used in limited framework. I both borrow this concept and reconsider it by referring to other scholars' methods of classification. From that point, I explore and these views in relation to religious experience. How does religiosity develop, maturity of religious faith take place, deep awareness of truth reveal itself, or an attitude of open-mindedness arise? After these states are realized, is interreligious agreement possible? Most religious studies scholars point out 'religious experience.' They say people could develop their faith from superficial religious beliefs into a more mature and deeper faith through religious experience while continuously aspiring towards enlightenment and practicing their religion in daily life. This study will try to examine aspects of superficial religious perspectives and deep religious perspectives represented in each religion and also explore criticism of each religion. With this view of superficial religious perspectives and deep religious perspectives, some cases documenting the religious experience of Daesoonjinrihoe disciples will be analyzed to see how their religiosity develops from superficial religious perspectives into deep religious perspectives through certain religious experiences. The characteristics of those experiences will also be investigated.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.