• Sim-seong Yeon-gu
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• v.33 no.2
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• pp.141-194
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• 2018

The article is about Yong-Ho-Bi-Gyeol(龍虎秘訣), which is one of the most important Taoist text in Korea written by Jeong-Ryum, a Taoist and alchemist in the Chosun Dynasty. The article deals with the alchemical and psychological meanings of Yong-Ho (龍虎, Dragon-Tiger), the way of nurturing cinnabar (修丹之道), the closing of the qi (閉氣), the method of alchemical breathing, the Dantian (丹田, cinnabar-field), and the Mysterious Female's One Opening (玄牝一竅), in addition to the brief introduction of the life of Jeong-Ryum and the bibliography of the book. The Yong-Ho (龍虎) meaning the dragon and tiger is the archetype of transformation in the form of their opposites, rooted in the psychoid system of the human psyche. The unified Yong-Ho makes Dan and the Dan, literally indicating cinnabar, has many alchemical connotations such as Mercurius, the rubedo state of the alchemical process, and the philosopher's stone. In the book, Jeong-Ryum emphasized the slow and subtle way of breathing in and out of Dantian to develop neidan (内丹, inner cinnabar or inner alchemy). The refining of neidan begins by the closing of the qi, which symbolizes the radical introversion and withdrawal of all the projections on the outer objects. The Dantian located at the lower part of the abdomen has been known to preserve jing (精), the vital essence of life, which can be refined into qi and spirit (神). In Jungian perspective, the Dantian is a mandala where an individual's mind can stay and focus at the center of psyche detached from ego and related to the Self. The long-nurtured introverted energy makes the Mysterious Female's One Opening (玄牝一竅), a pit or cavity in the transcendental space, through which the meditator can have a relationship with the great female principle of the universe. The current article has introduced the contents of the Yong-Ho-Bi-Gyeol in the perspective of analytical psychology. However, it has not dealth with the remaining topics including Taesik (胎息, embryonic breathing) and Juchenhwahu (周天火候, the great Celestial circuit firing), due to the lack of author's sufficient knowledge and experience. The unexplored areas of Yong-Ho-Bi-Gyeol will be studied in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.